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1. Integration of High Resolution Multitemporal Satellite Imagery for Improving Agricultural Crop Classification: a Case StudyTimely and accurate agriculture information is vital for ensuring global food security. Satellite imagery has already been proved as a reliable tool for remote crop mapping. Planet satellite imagery provides high cadence, global satellite coverage with higher temporal and spatial resolution than the Landsat-8 and Sentinel-2. This study examined the potential of utilizing high-resolution multitemporal imagery along with and normalized difference vegetation index (NDVI) to map the agricultural ... U. Ali, T. Esau, A. Farooque, Q. Zaman |
2. #DigitAg France#DigitAg, the Digital Agriculture Convergence Laboratory, is one of 10 French Convergence Institutes financed by the Investissements d'Avenir (Investment for the Future) program. #DigitAg conducts interdisciplinary research between agronomic sciences, engineering sciences (computer science, mathematics, electronics, physics, etc.) and social and management sciences (economics, sociology, business management), bringing together more than 700 experts in these fields to produce the scientifi... J. Taylor |
3. A 3D Camera-based Fertilizer Residue Monitoring System with Isobus for Precision AgricultureAccurate monitoring of fertilizer usage is critical for effective variable-rate fertilization (VRF), contributing to optimized nutrient management and environmental sustainability. VRF systems typically use open-loop control based on prescription maps and calibration data. However, this approach can introduce discrepancies between targeted and actual fertilizer application rates due to variations in fertilizer characteristics and environmental conditions. This study proposes a fertilizer hopp... C. Yun |
4. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical DataBayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri |
5. A Dilution-free Capacitive Sensing Platform for Rapid Detection of Honey AdulterationHoney adulteration has become increasingly prevalent, and consumers cannot easily verify authenticity without relying on specialized testing laboratories. Such approaches are time consuming and labor intensive, creating barriers to routine quality assurance. To streamline authenticity assessment, this study introduces a capacitive sensor as an alternative to conventional electrochemical impedance spectroscopy. The sensor directly interrogates undiluted honey and adulterated samples, eliminati... Y. Kung |
6. A Field Machine for Automated Quantification of Sweet Potato Phenotypic TraitsSweet potato is a globally important food crop, and its breeding is essential for enhancing nutritional value, ensuring food security, and promoting sustainable agriculture. However, the current process of parental selection largely depends on manual visual assessment, which is time-consuming and subject to human bias, thereby limiting both the efficiency and accuracy of breeding programs. In this work, a field machine for automated quantification of sweet potato phenotypic traits was propose... S. Hsieh |
7. A Framework for Imputation of Missing Parts in UAV Orthomosaics Using Planetscope and Sentinel-2 DataIn recent years, the emergence of Unmanned Aerial Vehicles (UAV), also known as drones, with high spatial resolution, has broadened the application of remote sensing in agriculture. However, UAV images commonly have specific problems with missing areas due to drone flight restrictions. Data mining techniques for imputing missing data is an activity often demanded in several fields of science. In this context, this research used the same approach to predict missing parts on orthomosaics obtain... F.R. Pereira, A.A. Dos reis, R.G. Freitas, S.R. Oliveira, L.R. Amaral, G.K. Figueiredo, J.F. Antunes, R.A. Lamparelli, E. Moro, N.D. Pereira, P.S. Magalhães |
8. A Generative Adversarial Network-based Method for High Fidelity Synthetic Data AugmentationDigital Agriculture has led to new phenotyping methods that use artificial intelligence and machine learning solutions on image and video data collected from lab, greenhouse, and field environments. The availability of accurately annotated image and video data remains a bottleneck for developing most machine learning and deep learning models. Typically, deep learning models require thousands of unique samples to accurately learn a given task. However, manual annotation of a large dataset will... S. Sridharan, S. Sornapudi, Q. Hu, S. Kumpatla, J. Bier |
9. A Hyperlocal Machine Learning Approach to Estimate NDVI from SAR Images for Agricultural FieldsThe normalized difference vegetation index (NDVI) is a key parameter in precision agriculture used globally since the 1970s. The NDVI is sensitive to the biochemical and physiological properties of the crop and is based on the Red (~650 nm) and NIR (~850 nm) spectral bands. It is used as a proxy to monitor crop growth, correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions which might al... R. Pelta, O. Beeri, T. Shilo, R. Tarshish |
10. A Low-cost Multi-view Image to 3d Reconstruction for Plant PhenotypingCurrent 3D plant phenotyping approaches often rely on LiDAR or multi-camera systems, which are costly, require complex calibration, and lack scalability. This study introduces a simple and cost-effective pipeline for 3D plant reconstruction using Hunyuan3D-2.5, a multi-view generative model. Plant samples were photographed directly using a mobile phone, and raw images were processed with a custom Python background-removal pipeline that enhanced plant contours and removed environmental noise. ... C. Huang |
11. A Low-tech Approach to Manage Within Field Variability – Toward a Territorial Scale ApplicationManaging within field variability is promising to achieve European objectives of sustainability in crop production. Technological development has allowed to precisely characterize fields heterogeneity in space and time. However, learnings from low adoption of yield maps in west-European context have highlighted the importance of reliable methods to support decisions. Blackmore et al. designed a delineation method considering yield as an integrative variable that reflects spatial and ... A. Lenoir, B. Vandoorne, B. Dumont |
12. A New Paradigm of Datadriven Agrifood Systems“Data-driven agrifood systems” is issued as a new standard terminology of smart farming from the international organization for standardization (ISO), and it has also focused on the needs of small/medium enterprises of farming. Data management scheme has changed the context of decision making on received style of good agricultural practices. Farmers and stakeholders should re-watch the system changes with emerging technologies. Farm management sustainable and community-based shoul... S. Shibusawa |
13. A Passive-RFID Wireless Sensor Node for Precision AgricultureAccurate soil data is crucial for precision agriculture. While existing optical methods can correlate soil health to the gasses emitted from the field, in-soil electronic sensors enable real-time measurements of soil conditions at the effective root zone of a crop. Unfortunately, modern soil sensor systems are limited in what signals they can measure and are generally too expensive to reasonably distribute the sensors in the density required for spatially accurate feedback. In thi... P.J. Goodrich, C. Baumbauer, A.C. Arias |
14. A Physics-informed Neural Network Approach for Simulating Laminar FlowEfficient and accurate modeling in agricultural fields is critical for advancing precision agriculture. These simulations, often involving the prediction of airflow, temperature, and humidity distributions, directly support decisions related to crop management, greenhouse climate control, and irrigation strategies. Computational Fluid Dynamics (CFD) has been a primary tool for decades, offering reliable and high-fidelity simulations through established numerical methods such as the finite-dif... C. Huang |
15. A Rapid Non-invasive Capacitive Platform for in Vitro Assessment of Insecticide- Induced Skin CorrosionThis study introduces a rapid, non-invasive, and highly sensitive method for evaluating skin corrosion. The platform combines a capacitive sensor with a screen-printed electrode coated in a skin-mimetic layer, allowing real-time monitoring of capacitance changes in surrogate skin before and after exposure to corrosive agents such as agricultural insecticides. The biomimetic coating, formulated from hexane, ethanol, and lanolin, reproduces the lipid composition of the human stratum corneum. Ac... Y. Kung |
16. A Review on Structural Enhancements and Domain-Specific Adaptation of YOLO for Crop-Weed RecognitionThis review systematically summarizes YOLO-based weed detection models, focusing on two key directions: attention mechanisms that improve discrimination between visually similar vegetation and lightweight techniques that ensure real-time performance on limited hardware. A comparative analysis of improved YOLO variants highlights how structural optimizations improve detection, offering insights into efficient model design. ... J. Park |
17. A Simulation-based Matching System for Utilizing Clean Energy from Agri-livestock WasteIn order to mitigate greenhouse gas emissions and air pollution derived from agricultural and livestock waste and to enhance the resilience of the clean energy supply chain, a simulation-based matching system for utilizing clean energy from agri-livestock waste as developed. Building upon a prior research entitled " An Inventory of Greenhouse Gases and a Map of Biomass Energy Utilization in Agriculture and Animal Husbandry Biomass Waste," the system is designed to evaluate the effic... J. Jiang |
18. A Vision-guided Gantry Robot for Efficient Orchid Basket Reorganization in GreenhousesProper alignment of orchid baskets in greenhouses is important to maintain visual uniformity, maximize space usage, and ensure consistent light exposure during flowering. Manual arrangement is time-consuming and labor-intensive, underscoring the need for automation. To address this challenge, we propose an integrated robotic system for automated basket organization. The system combines a cartesian gantry robot, a rotation-aware clamping gripper, dual Intel RealSense D435i cameras, and a light... W. Lin |
19. Adoption of Precision Agriculture in JapanJapan is a country facing global challenges in terms of a declining and aging agricultural population, making the establishment of a sustainable production system a matter of urgency from the perspective of food security. While respecting Japan's traditional knowledge, the author believes that precision agriculture is an effective solution to resolve this situation. We argue that data-driven agriculture presents a higher degree of affinity with Japanese farmers, providing a more viable pa... E. Morimoto |
20. AgDataBox-IoT Application Development for Agrometeorogical Stations in Smart FarmCurrently, Brazil is one of the world’s largest grain producers and exporters. Brazil produced 125 million tons of soybean in the 2019/2020 growing season, becoming the world’s largest soybean producer in 2020. Brazil’s economic dependence on agribusiness makes investments and research necessary to increase yield and profitability. Agriculture has already entered its 4.0 version, also known as digital agriculture, when the industry has entered the 4.0 era. This new paradigm ... A. Hachisuca, E.G. Souza, E. Mercante, R. Sobjak, D. Ganascini, M. Abdala, I. Mendes, C. Bazzi, M. Rodrigues |
21. AgDataBox: Web Platform of Data Integration, Software, and Methodologies for Digital AgricultureAgriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. Digital agriculture enables the flow of informatio... E.G. Souza, C. Bazzi, A. Hachisuca, R. Sobjak, A. Gavioli, N. Betzek, K. Schenatto, E. Mercante, M. Rodrigues, W. Moreira |
22. Agricultural Robots Classification Based on Clustering by Features and FunctionRobotic systems in agriculture (hereafter referred to as agrobots) have become popular in the last few years. They represent an opportunity to make food production more efficient, especially when coupled with technologies such as the Internet of Things and Big Data. Agrobots bring many advantages in farm operations: they can reduce humane fatigue and work-related accidents. In contrast, their large-scale diffusion is today limited by a lack of clarity and exhaustiveness in the regulatory fram... M. Canavari, M. Medici, G. Rossetti |
23. Agriculture Machine Guidance Systems: Performance Analysis of Professional GNSS ReceiversGNSS (Global Navigation Satellite Systems) plays nowadays a major role in different civilian activities and is a key technology enabling innovation in different market sectors. For instance, GNSS-enabled solutions are widespread within the Precision Agriculture and, among them, applications in the field of machinery guidance are commonly employed to optimize typical agriculture practices. The scope of this paper is to present the outcomes of the agriculture testing campaign performe... J. Capolicchio, D. Mennuti, I. Milani, M. Fortunato, R. Petix, J. Reyes gonzalez, M. Sunkevic |
24. Agronomic Opportunities Highlighted by the Hands Free Hectare and Hands Free Farm Autonomous Farming ProjectsWith agriculture facing various challenges including population increase, urbanisation and both mitigating and managing climate change, agricultural automation and robotics have long been seen as potential solutions beyond precision farming. The Hands Free Hectare (HFH) and Hands Free Farm (HFF) collaborative projects based at Harper Adams University (HAU) have been developing autonomous farming systems since 2016 and have conducted multiple autonomous field crop production cycles since a wor... K.F. Franklin |
25. AI for Genomic Agriculture — from Sequence to Field ImpactGenomics offers powerful opportunities to enhance crop yield, resilience, and nutritional value, yet the complexity and scale of genomic, transcriptomic, and epigenomic data pose significant challenges for interpretation and application. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful approaches to decode this complexity and accelerate precision agriculture. I will present AI-based methods developed in my laboratory for annotating pla... C. Chen |
26. AI for Genomic Agriculture — from Sequence to Field ImpactGenomics offers powerful opportunities to enhance crop yield, resilience, and nutritional value, yet the complexity and scale of genomic, transcriptomic, and epigenomic data pose significant challenges for interpretation and application. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful approaches to decode this complexity and accelerate precision agriculture. I will present AI-based methods developed in my laboratory for annotating pla... C. Chen |
27. AI-driven Evapotranspiration Prediction Using Plant Wearable Sensor for Smart IrrigationThe global expansion of greenhouse cultivation has created a need for reliable crop evapotranspiration (ET) estimation to enable precise irrigation, thereby improving yields, enhancing crop quality, and addressing challenges related to water scarcity and environmental sustainability. This study proposes the development of a plant signal-based artificial intelligence (AI) model for ET prediction, tailored to the unique environmental conditions of greenhouse systems. Unlike empirical models tha... W. Cho |
28. An Intelligent Blade Balancing Control System for Steep-terrain Tea Cutting ApplicationsTea is a famous and valuable beverage in Taiwan. Tea is mainly grown in steep or mountainous areas. The terrain is a challenge for harvesting automation. Manual labor in harvesting tea in complex terrain is time-consuming and affects the economic efficiency of the product. This study proposes a tea-cutting blade balancing control system integrating image processing and fuzzy logic control. A unique mechanism is developed to adapt to the slope of the terrain. The limiting angle is 12 degrees r... W. Lin |
29. An Intelligent Poultry Health Monitoring System Based on Multimodal Sensing TechnologiesTraditional poultry farming primarily relies on manual observation to assess the chicken flock health status. However, this approach is not only time-consuming and labor-intensive but also highly dependent on individual experience, making real-time monitoring difficult to achieve. With the advanced Internet of Things (IoT) and Artificial Intelligence (AI) technologies, the animal industry is gradually transitioning toward intelligent farming. At present, most sensors focus primarily on enviro... Y. Shih |
30. An IoT-based Smart Real Time Sensing and Control of Heavy Metals to Ensure Optimal Growth of Plants in an Aquaponic Set-upThe concentration of heavy metals that needs to be maintained in aquaponic environments for habitable growth of plants has been a cause of concern for many decades now as it is not possible to eliminate them completely in a commercial set-up. Our goal is to design a cost-effective real-time smart sensing and actuation system in order to control the concentration of heavy metals in aquaponic solutions. Our solution consists of sensing the nutrient concentrations in the aquaponic solution, name... S. Dhal, J. Louis, N. O'sullivan, J. Gumero, M. Soetan, S. Kalafatis, J. Lusher, S. Mahanta |
31. Analysis Of Internal Abnormalities Of Tilapia Flesh Using Hyperspectral Imaging And Machine Learning MethodTilapia, the most produced aquaculture species in Taiwan, has experienced significant production loss due to internal abnormalities, notably streptococcosis, which remains undetectable until fillets are cut. The absence of visible external symptoms frequently leads to quality reduction and economic loss. To address this, hyperspectral imaging, capable of capturing subtle spatial and spectral differences, was employed. The objective of this study was divided into two phases: firstly, identific... S. Chen |
32. Analysis of the Mapping Results Using SoilOptix TM Technology in Chile After Two SeasonsSoil mapping is a key element to successfully implement Integrated Nutrient Management (INM) in high value crops. SoilOptixTM is a mapping service based on the use of gamma radiation technology that arrived in Chile in 2019. Since then, around 2000 ha have been mapped, mainly in fruit orchards and vineyards. The technology has demonstrated its value in determining the most limiting factors in new and old orchards, and the possibility of correcting them in a site-spe... R.A. Ortega, A.F. Ortega, M.C. Orellana |
33. Analytical and Technological Advancements for Soybean Quality Mapping and Economic DifferentiationIn the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses. Modern on-the-go near-infrared (NIR) sensing technologies during the harvest and proximal remote sensing (aerial and satellite imagery) before harvest time can be used to provide an early estimate of seed quality levels, benchmark in-season predictions with at-harvest final seed quality and enable seed differentiation for farmers leading to better marketing strategie... A. Prestholt, C. Hernandez, I. Ciampitti , P. Kyveryga |
34. Apple Weight Prediction Based on Lifecycle Growth InformationIt is essential for determining optimal harvest timing, the grade of quality, and fresh maintenance, all of which directly impact growers’ economic returns, to accurately predict individual apple fruit weight. This study aims to predict the fruit weight of Fuji apples at the main branch level (n = 126) using growth data collected throughout the growing season. Fuji apples were monitored at 23 orchards in 2022 and 2023, and at 24 orchards in 2024. Growth data were col... C. Ryu |
35. Application of Automation Technology in Mushroom Stem-cutting: a Case Study from Xinshe, TaiwanThe Xinshe region is one of Taiwan’s major mushroom production hubs, renowned for its high annual yield and exceptional quality. However, despite the growing demand in both domestic and international markets, the region faces a lack of comprehensive automated processing equipment, limiting productivity and exacerbating labor shortages. To address these challenges, this study applies advanced automation technology to develop a mushroom stem-cutting machine aimed at enhancing production e... K. Li |
36. Application of Deep Learning for Symptom Detection and Localization in Phalaenopsis PlantletsPhalaenopsis plantlets in dense greenhouses are vulnerable to diseases like soft rot, which spreads rapidly. This study compares YOLOv11 with enhanced architectures (FasterNet, MambaVision) for symptom detection and localization. Single- and multi-model strategies were evaluated for disease recognition, plant segmentation, and keypoint localization, enabling robotic removal and efficient automated disease management. ... Y. Huang |
37. Application of Drone Data to Assess Damage Intensity of Bacterial Leaf Blight Disease on Rice Crop in IndonesiaThe Government of Indonesia has launched agricultural insurance program since 2016. A key in agricultural insurance is damage assessment which is required to be as precise, quick, quantitative and inexpensive as possible. Current method is to inspect the damage by human eyes of specialist having experiences. This method, however, costs much and is difficult to estimate disease infected fields precisely in wide area. So, there is increasing need to develop effective, simplified and low cost me... C. Hongo, S. Isono, G. Sigit, B. Utoyo, E. Tamura |
38. Application of Image Processing and Artificial Inteligence (AI) for Cabbage Cultivation MonitoringCabbage requires precise monitoring for during cultivation, e.g., transplanting performance, water stress, growth status, and yield estimation. This study presents image processing and artificial intelligence (AI) techniques to enhance automation for cabbage production operations. High-resolution multispectral and thermal images were acquired using UAVs and ground-based platforms. Seedling detection during transplanting operation was implemented using a YOLOv8 model with a CSPDarknet53 backbo... S. Chung |
39. Applying Retrieval-augmented-generation to Support Farmers in Pest and Disease DiagnosisAccording to the Ministry of Agriculture, crop production in Taiwan reached a value of $275 billion NTD in 2023, highlighting the economic importance of agriculture. However, the industry is now facing serious challenges, particularly in pest and disease identification and crop protection. Due to global ecological challenges, the manifestations of local pests and diseases have changed, making it difficult for farmers to rely on past experiences to identify and manage them effectively. Farmers... Y. Kuo |
40. Assessing the Potential of Sentinel-1 in Retrieving Mango Phenology and Investigating Its Relation to Weather in Southern GhanaThe rise in global production of horticultural tree crops over the past few decades is driving technology-based innovation and research to promote productivity and efficiency. Although mango production is on the rise, application of the remote sensing technology is generally limited and the available study on retrieving mango phenology stages specifically, was focused on the application of optical data. We therefore sought to answer the questions; (1) can key phenology stages of mango be retr... B.A. Torgbor, M.M. Rahman, A. Robson, J. Brinkhoff |
41. Assessment of Active Crop Canopy Sensor As a Tool for Optimal Nitrogen Management in Dryland Winter WheatOptimum nitrogen (N) fertilizer application is important for agronomic, economic, and environmental reasons. Among different N management tools, active crop canopy sensors are a recent and promising tool widely evaluated for use in corn but still under-evaluated for use in winter wheat. The objective of this study was to determine whether vegetation indices derived from in-season active crop canopy sensor data can be used to predict winter wheat grain yield and protein content and subsequentl... D. Ghimire |
42. Assessment of Goss Wilt Disease Severity Using Machine Learning Techniques Coupled with UAV ImageryGoss Wilt has become a common disease in corn fields in North Dakota. It has been one of the most yield-limiting diseases, causing losses of up to 50%. The current method to identify the disease is through visual inspection of the field, which is inefficient, and can be subjective, with misleading results, due to evaluator fatigue. Therefore, developing a reliable, accurate, and automated tool for assessing the severity of Goss's Wilt disease has become a top priority. The use of un... A. Das, P. Flores, Z. Zhang , A. Friskop, J. Mathew |
43. Assessment of Light Interception Considering Plant Architecture is Important for Yield Prediction in StrawberryCrop yield depends on whole‒plant photosynthesis, which is limited by the light interception by each leaf and its individual photosynthetic capacity. To date, there are some researches on assessments of yield considering their plant architecture and photosynthetic capacities in tomato and cucumber. However, there are few in strawberry although its cultivars exhibit considerable variation in their plant architecture and photosynthetic capacity. This research gap could significantly hinder ac... D. Yasutake |
44. Automated Analysis of Dairy Cow Bedding Behavior Patterns Based on Deep Learning TechnologyStalls are among the most frequently used facilities for dairy cows. Their design and use directly affect the cows’ health, comfort, and willingness to lie down. These factors are closely associated with both animal welfare and milk production. Therefore, monitoring stall use provides a practical basis for evaluating stall design and barn environment quality, in line with the Five Freedoms of Animal Welfare. This study proposes an automated system based on deep learning to analyze stall... C. Chang |
45. Automated Geometrical Field Boundary Delineation Algorithm for Adjacent Job SitesEstablishing farmland geometric boundaries is a critical component of any assistive technology, designed towards the automation of mechanized farming systems. Observing farmland boundaries enables farmers and farm machinery contractors to determine; seed purchase orders, fertiliser application rate, and crop yields. Farmers must supply acreage measurements to regulatory bodies, who will use the geometric data to develop environmental policies and allocate farm subsidies appropriately. Agricu... S.J. Harkin |
46. Automated Identification of Tomato Diseases, Pests, and Disorders Using Ai Models and Smartphone ApplicationsTomato is one of the most important economic crops in many countries, with a substantial global production volume. However, tomato growth is often affected by diseases, pests, and physiological disorders (DPD), which typically manifest as symptoms on leaves, such as specks, yellowing, necrosis, or leaf deformation. These issues significantly reduce tomato yield and quality. Therefore, accurately identifying these symptoms and implementing corresponding management strategies have become crucia... Y. Kuo |
47. Automated Lag Phase Detection in Wine GrapesCrop yield estimation, an important managerial tool for vineyard managers, plays a crucial role in planning pre/post-harvest operations to achieve desired yield and improve efficiency of various field operations. Although various technological approaches have been developed in the past for automated yield estimation in wine grapes, challenges such as cost and complexity of the technology, need of higher technical expertise for their operation and insufficient accuracy have caused major concer... P. Upadhyaya, M. Karkee, X. Zhang, S. Kashetri |
48. Automated Quality Determination of Broccoli and Cauliflower Using Deep LearningBroccoli and cauliflower have a narrow harvesting window, making accurate quality assessment essential for determining optimal harvest timing. This study developed specific grading models to automatically determine the quality of broccoli and cauliflower by three phenotypic indicators: color, shape, and maturity, using deep learning methods. About 600 top-view field images of broccoli and cauliflower were collected under natural conditions, and all annotations were cross-checked and verified ... S. Chen |
49. Automated Selection of Taiwan Native Breeding Chickens Using Machine Vision and Deep LearningChicken is a primary global source of protein. In Taiwan, the poultry sector is a cornerstone of the domestic food supply. A significant part of this sector is the Taiwan Native Chicken (TNC), a collection of indigenous breeds prized for their unique flavor and cultural value, generating over 26 billion New Taiwan Dollars in 2023. Maintaining the quality of TNC relies on the effective selection of superior breeders. Conventionally, this selection is performed through manual inspection of phen... Y. Kuo |
50. Automatic Air-exchange Whole-plant Carbon Sequestration Measurement System in Potted Native Taiwanese Plant ApplicationsAs the climate crisis accelerates, reliable data on the carbon‐sequestration capacity of native plants are needed to guide nature-based mitigation strategies. Accurate, whole-plant assessments of carbon sequestration are crucial for identifying native species that can efficiently reduce atmospheric carbon dioxide, yet most plant physiology studies rely on costly instruments limited to leaf-level measurements. We therefore designed a low-cost, closed-chamber system that automatically refresh... L. Chien |
51. Automatic Calibration of Crop Growth Models for Predicting Corn Economic Optimum Nitrogen RatesThe objectives of this study were to 1) evaluate an automatic model calibration strategy; and 2) compare the performance of DSSAT and APEX models for simulation of maize (Zea mays L.) growth, plant nitrogen (N) uptake, yield in response to different N application rates and the estimation of the economic optimum N rate (EONR). Detailed data collected from eight site- years of N experiments conducted from 2014 to 2016 in Minnesota and Wisconsin, USA were used in this research. The results indic... Y. Miao |
52. Automatic Counting of Chickens Around Feeders Using Convolutional Neural NetworksIn 2023, Taiwan’s chicken industry generated about NTD 93.6 billion, or 43.5% of the livestock production value, underscoring its central role in the sector. Nonetheless, monitoring flock health and housing remains labor-intensive, and adjustments to feeding regimes frequently depend on subjective judgment, limiting standardization and scalability. Because feeding behavior is a key indicator of health and welfare, we present a vision-based system that continuously detects feeders and co... Y. Kuo |
53. Biosynthesis of Silver Nanoparticles from Platostoma Palustre for Agricultural ApplicationsNanoparticle synthesis using natural resources offers a cost-effective and eco-friendly strategy. In this study, silver nanoparticles (AgNPs) were synthesized using Platostoma palustre extract (PPE), rich in polysaccharides and bioactive compounds. Characterization by XRD, SEM, and TEM confirmed successful synthesis. TEM revealed oval-shaped nanoparticles (7-80 nm) with PPE forming a stabilizing layer to prevent agglomeration, while XRD indicated a crystallite size of approximately ... W. Lin |
54. Cabbage Yield Estimation Using Multispectral UAV Imagery and Deep Neural SegmentationAccurate and efficient yield estimation is essential of optimizing crop management, resource allocation, and harvest planning in precision agriculture. Traditional manual methods are time-consuming, labor-intensive, and often lack spatial accuracy. Recent advances in remote sensing and deep learning offer scalable, non-destructive alternatives for yield monitoring. This study proposed a cabbage yield estimation based on an enhanced unity networking (U-Net) segmentation model utilizing multisp... S. Chung |
55. Can Topographic Indices Be Used for Irrigation Management Zone DelineationSoil water movement is affected by soil physical properties and field terrain changes. The identification of within-field areas prone to excess or deficit of soil moisture could support the implementation of variable rate irrigation and adoption of irrigation scheduling strategies. This study evaluated the use of the topographic wetness index (TWI) and topographic position index (TPI) to understand and explain within-field soil moisture variability. Volumetric water content (VWC) collected in... B.V. Ortiz, B.P. Lena, F. morlin , G. Morata, M. Duarte de val, R. Prasad, A. Gamble |
56. Cfd Evaluation of Uvc Air-cleaning Integration in Greenhouse Hvac SystemsGreenhouse crops in Taiwan are highly vulnerable to airborne pathogens due to the humid climate and poor ventilation. This study evaluated the integration of UVC air- cleaning devices with the greenhouse HVAC system to reduce pathogen concentrations. A SolidWorks model of the NTU smart greenhouse was constructed, and CFD simulations were conducted to compare three configurations in which four UVC units were placed at the upper, middle, and lower regions of the wet pad. Results showed that the... C. Huang |
57. Changes in Soil Quality when Building Ridges for Fruit PlantationMany fruit plantations are usually performed in ridges for various reasons including, escaping from a clay horizon, improving overall soil quality and drainage, among others. Normally ridges are built using the surface horizons, producing a mixture of soils layers, and therefore changing the quality of the soil at the rooting zone. We were interested in studying the changes in soil properties when building ridges in a flat alluvial soil that was planted with avocado. A det... H.P. Poblete, R.A. Ortega |
58. Close-range Remote Sensing Data for Optimizing Horticultural Production ProcessesPlant sensors have been explored over the last three decades, resulting in various non-destructive sensor systems, feasible for usage along the entire horticultural supply chain. This review will show examples of sensor applications, pointing out benefits and challenges of different measuring principles. Particular emphasis is given on recent developments on analyzing plants directly in the field, aiming precise, data-driven production measures. ... M. Zude-sasse |
59. Cloud Correction of Sentinel-2 NDVI Using S2cloudless PackageOptical satellite-derived Normalized Difference Vegetation Index (NDVI) is by far the most commonly used vegetation index value for crop monitoring. However, it is quite sensitive to the cloud, and cloud shadows and significantly decreases its usability, especially in agricultural applications. Therefore, an accurate and reliable cloud correction method is mandatory for its effective application. To address this issue, we have developed an approach to correct the NDVI values of each and every... A. Saxena, M. Dash, A.P. Verma |
60. Coarse-to-fine Navigation for Robotic Feed Delivery in Precision Livestock FarmingThis paper introduces an autonomous mobile robot designed for automated feed replenishment at multiple designated stations. The robot employs a hierarchical navigation strategy, enabling both wide-area coarse positioning and precise localization at target feeding stations. For global navigation, the robot fuses data from a pre-defined map grid, odometry, and an electronic compass. This multi-sensor data is integrated using a least squares method (LSM) to ensure robust coarse position estimati... C. Chang |
61. Color Identification and Texture Features of Phalaenopsis Using Deep LearningAs one of the most economically important and widely traded ornamental plants worldwide, Phalaenopsis hold a significant position in the global floriculture industry. The breeding process is traditionally labor-intensive, requiring careful visual assessment of numerous floral traits to select desirable varieties, which underscores the need for scalable, automated solutions. To enhance the efficiency of Phalaenopsis breeding and accelerate phenotypic comparison across varie... Y. Kuo |
62. Comparative Analysis of Light-weight Deep Learning Architectures for Soybean Yield Estimation Based on Pod Count from Proximal Sensing Data for Mobile and Embedded Vision ApplicationsCrop yield prediction is an important aspect of farming and food-production. Therefore, estimating yield is important for crop breeders, seed-companies, and farmers to make informed real-time financial decisions. In-field soybean (Glycine max L.(Merr.)) yield estimation can be of great value to plant breeders as they screen thousands of plots to identify better yielding genotypes that ultimately will strengthen national food security. Existing soybean yield estimation too... J.J. Mathew, P.J. Flores, J. Stenger, C. Miranda, Z. Zhang, A.K. Das |
63. Comparison and Validation of Different Soil Survey Techniques to Support a Precision Agricultural SystemThe data need of precision agriculture has resulted in an intensive increase in the number of modern soil survey equipment and methods available for farmers and consultants. In many cases these survey methods cannot provide accurate information under the used environmental conditions. On a 36 hectare experimental field, several methods have been compared to identify the ones which can support the PA system the best. The methods included contact and non contact soil scanning, yield mapping, hi... V. Lang, G. Tóth, S. Csenki, D. Dafnaki |
64. Comparison of Canopy Extraction Methods from UAV Thermal Images for Temperature Mapping: a Case Study from a Peach OrchardCanopy extraction using thermal images significantly affects temperature mapping and crop water status estimation. This study aimed to compare several canopy extraction methodologies by utilizing a large database of UAV thermal images from a precision irrigation trial in a peach orchard. Canopy extraction using thermal images can be attained by purely statistical analysis (S), a combination of statistical and spatial analyses (SS), or by synchronizing thermal and RGB images, following RGB sta... L. Katz, A. Ben-gal, I. Litaor, A. Naor, A. Peeters, E. Goldshtein, V. Alchanatis, Y. Cohen |
65. Comparison of Different Aspatial and Spatial Indicators to Assess Performance of Spatialized Crop Models at Different Within-field ScalesMost current crop models are point-based models, i.e. they simulate agronomic variables on a spatial footprint on which they were initially designed (e.g. plant, field, region scale). To assess their performances, many indicators based on the comparison of estimated vs observed data, can be used such as root mean square error (RMSE) or Willmott index of agreement (D-index) among others. However, shifting model use from a strategic objective to tactical in-season management is becoming a signi... D. Pasquel, S. Roux, B. Tisseyre, J.A. Taylor |
66. Constraint of Data Availability on the Predictive Ability of Crop Response Models Developed from On-farm ExperimentationDue to the variability between fields and across years, on-farm experimentation combined with crop response modeling are crucial aspects of decision support systems to make accurate predictions of yield and grain protein content in upcoming years for a given field. To maximize accuracy of models, models fit using environmental covariate and experimental data gathered up to the point that crop responses (yield/grain protein) are fit repeatedly over time until the model can predict future crop ... P. Hegedus, B. Maxwell |
67. Cotton Boll Detection and Yield Estimation Using UAS Lidar Data and RGB ImageCotton boll distribution is a critical phenotypic trait that represents the plant's response to its environment. Accurate quantification of boll distribution provides valuable information for breeding cultivars with high yield and fiber quality. Manual methods for boll mapping are time-consuming and labor-intensive. We evaluated the application of Lidar point cloud and RGB image data in boll detection and distribution and yield estimation. Lidar data was acquired at 15 m using a DJI Matri... Z. Lin, W. Guo, N. Gill |
68. Coupling Machine Learning Algorithms and GIS for Crop Yield Predictions Based on Remote Sensing Imagery and Topographic IndicesIn-season yield prediction can support crop management decisions helping farmers achieve their yield goals. The use of remote sensing to predict yield it is an alternative for non-destructive yield assessment but coupling auxiliary data such as topography features could help increase the accuracy of yield estimation. Predictive algorithms that can effectively identify, process and predict yield at field scale base on remote sensing and topography still needed. Machine learning could be an alt... M.F. Oliveira, G.T. Morata, B. Ortiz, R.P. Silva, A. Jimenez |
69. Current Status and Potential of Digital Agriculture in IndiaIndian agriculture is facing multiple challenges, including climate change, resource depletion, low productivity growth, high post-harvest losses etc. To address these, a strong push toward digital and smart farming is underway. The Government of India has launched major initiatives such as the Digital Agriculture Mission, Digital India Programme, and targeted funding for AI, Machine Learning, and cyber security to support agricultural innovation. The focus is on building Digital Pu... M. Singh |
70. Data Sources and Risk Management in Precision AgricultureThe digitalisation of the agricultural economy provides more data about the biological processes and technological solutions used for producing agricultural products than ever before. Paralell to the data collection – aiming to provide information for agricultural decision-making and operations – the data informs the farmers, public administration officers and other players in agriculture about the state of the environment. The strategic planning on operation of farms and data han... G. Milics, P.M. Varga, F. Magyar, I. Balla |
71. Decision Support from On-field Precision ExperimentsEmpirically driven adaptive management in large-scale commodity crop production has become possible with spatially controlled application and sub-field scale crop monitoring technology. Site-specific experimentation is fundamental to an agroecosystem adaptive management (AAM) framework that results in information for growers to make informed decisions about their practices. Crop production and quality response data from combine harvester mounted sensors and internet available remote sensing d... B.D. Maxwell, P.D. Hegedus, S.D. Loewen, H.D. Duff, J.W. Sheppard, A.D. Peerlinck, G.L. Morales, A. Bekkerman |
72. Deep Learning Based Image Recognition for the Detection of Natural Behaviors in Laying HensEggs are an important source of protein, widely favored and needed by the public. The production and quality of eggs are closely related to the rearing environment of laying hens. Common rearing methods include conventional cages, enriched cages, floor systems, and free-range systems. Different housing environments may influence the production efficiency of hens. In recent years, increasing attention has been given to balancing animal welfare with production efficiency. Animal welfare is clos... C. Huang |
73. Deep Learning Prediction of Methane Production in Mesophilic and Thermophilic Anaerobic DigestionAnaerobic digestion (AD) converts organic waste into methane-rich biogas but forecasting methane yield is difficult due to nonlinear dynamics. This study compares Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), and Temporal Fusion Transformer （TFT） models for predicting methane production rate (MPR, L/L/d) under mesophilic (37°C) and thermophilic (55°C) conditions. Lab-scale reactor data with features including hydraulic r... C. Chou, A. Liu |
74. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS ImageryDeep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high re... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal |
75. Deep Learning-based Detection and Quantity of Livestock CarcassesIn recent years, livestock farming in Taiwan has gradually expanded. However, due to geographical constraints and regulatory restrictions, local farms are not permitted to bury or burn culled livestock carcasses on their own. As a result, farms must commission rendering trucks to collect and transport the carcasses to centralized rendering plants for processing. At the rendering plants, the manager are required to recount the number of carcasses. However, the large quantity and irregular arra... C. Chuang |
76. Deep Learning-based Insect Detection on Sticky Traps Captured Via Mobile Phones Under Field Lighting ConditionsInsect pests pose a major threat to agricultural production, requiring effective integrated pest management (IPM) strategies that depend on accurate identification and counting of pests captured on sticky traps. However, mobile phone images taken under natural field lighting often suffer from inconsistent illumination, shadow interference, and low visibility of small insect targets, which significantly reduce the reliability of automated monitoring systems. To address these challenges, this s... T. Lin |
77. Deep Learning-Based Vocal Signature Analysis for Rumination Belching IdentificationAnimal vocalizations provide important information about various behaviors, including courtship, parental care, foraging, and vigilance, as well as physiological states such as estrus, parturition, stress, and disease. In ruminants, eructation-related sounds are primarily produced when methane, generated by microbial fermentation of plant fibers in the rumen, is released orally. The acoustic characteristics of these sounds—such as frequency, intensity, and duration—are closely ass... C. Wu |
78. Deep Reinforcement Learning Based Robotic Arm Control for Autonomous HarvestingInverse Kinematics (IK) is a traditional method used for robotic arm manipulation, relying heavily on precise calibration and huge computational demands for arms with higher Degrees of Freedom (DoF). In contrast, Deep Reinforcement Learning (DRL) is an innovative approach to manipulation that exhibits greater tolerance for calibration inaccuracies. It trains using noise added to joint angles, allowing it to learn how to compute accurate trajectories even with inaccuracies in the joint angles.... C. Huang |
79. Delineation of Site-specific Management Zones with Proximal Data and Multi-spectral ImageryMany findings suggested that it’s possible to improve the accuracy of delineating site-specific management zones (SSMZs) through a combination of proximal data with remote sensing imagery. The objective of this study is to assess the feasibility of delineating SSMZs with a wide range of ancillary data (proximal survey and multi-spectral data). The study area is a 22.1acre located 10 miles north of Fort Collins, CO and is known for having a high spatial and temporal variability of soil p... W.A. Yilma, J. Siegfried, R. Khosla |
80. Design and Development of UECS-based Environmental Monitoring and Control Platform Without CodingData-driven agriculture has been increasingly adopted to achieve labor-saving, energy efficiency, and resource optimization in agricultural operations. Among small- and medium-scale horticul- tures, the Ubiquitous Environment Control System (UECS) proposed in 2004 is attracting again due to low cost of introduction. The UECS is an autonomous and distributed open-source en- vironmental monitoring and control platform for greenhouse horticulture. A computer called a node is used in each environ... T. Okayasu |
81. Design and Performance Analysis of a Flexible Lig Thin-film HeaterGas sensors play vital roles in environmental, agricultural, and industrial monitoring, yet metal-oxide sensors require high operating temperatures. Conventional ceramic or metallic heaters consume high power and lack flexibility, limiting portable applications. Here, flexible thin-film heaters were fabricated using laser-induced graphene (LIG) on polyimide substrates. Two patterns with varied laser powers and scanning speeds were tested, and electrical and thermal performances evaluated. Hig... X. Ji |
82. Design of a Collision Avoidance Algorithm for Autonomous Tractors with ImplementsOver the past decade, autonomous tractors have emerged as a key technology in agricultural automation. Global Navigation Satellite System (GNSS)-based navigation is widely used in autonomous tractors. However, since the GNSS cannot perceive the surroundings, an additional perception system is required to ensure the safety of the operation. Paddy ridges, one of the major obstacles in paddy fields, are typically higher than farmland to facilitate water storage. These height differences can lead... H. Kim |
83. Design of a Garlic Seeding Monitoring and Mapping System Using GNSS and Vision SensorsSeeding monitoring serves as the first step in precision agriculture, playing a crucial role in collecting and managing data across the entire agricultural process. While several international companies have recently developed precision agriculture solutions that monitor seeding rate, missing rate, and more, the agricultural environment in Korea presents unique challenges. For instance, in the case of Korean garlic planters, an average missing rate of approximately 10% is observed. When these... H. Kim |
84. Detect Estrus in Sows Using a Lidar Sensor and Machine LearningAccurate estrus detection of sows is labor intensive and is crucial to achieve high farrowing rate. This study aims to develop a method to detect accurate estrus time by monitoring the change in vulvar swollenness around estrus using a light detection and ranging (LiDAR) camera. The measurement accuracy of the LiDAR camera was evaluated in laboratory conditions before it was used in monitoring sows in a swine research facility. In this study, twelve multiparous individually housed sows were c... J. Zhou, Z. Xu |
85. Detecting and Removing Defective Carcasses of Taiwanese Native Chickens Using Convolutional Neural NetworksPoultry is one of the most important sources of meat worldwide. In 2023, the production value of poultry in Taiwan reached 59.8 billion NTD, accounting for 27.8% of the economic value of the animal husbandry industry. Among various chicken breeds, Taiwanese native chickens (TNC) are highly favored by consumers for their meat quality and flavor. As the demand for chicken increases, providing high quality meat to the market has become crucial. Unlike broilers, Taiwanese native chickens have div... Y. Kuo |
86. Determining the Marginal Value of Extra Precision in Precision Grazing Systems – an Ex Ante Analysis of Impacts on System Productivity, Sustainability and EconomicsThe development of precision livestock farming (PLF) technologies for application in grazing systems is rapidly evolving. PLF technologies that facilitate the spatial and temporal management of variability in landscapes, pastures and animals promise to improve the efficiency, profitability and sustainability of livestock farming. However, such technologies as a complete package do not yet exist in grazing systems and the question of impacts at the farm system level remains unresolved. Other p... K. Behrendt, T. Takahashi, M.S. Rutter |
87. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US MidwestEffective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly acr... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan |
88. Developing a neural-network model for detecting Aflatoxin hotspots in peanut fieldsAflatoxin is a carcinogenic toxin produced by a soilborne fungi, called Aspergillus flavus, causing a difficult struggle for the peanut industry in terms of produce quality, price and the range of selling market. This study aims to develop a successful U-Net CNN (Convolutional Neural Network) model, a reliable image segmentation method, that will help in distinguishing high probability zones of occurrence of Aflatoxin in peanut fields using remotely sensed hyperspectral imagery. The research ... S. Kukal, G. Vellidis |
89. Developing Empirical Method to Estimate Phosphorous in Potato Plants Using Spectroscopy-based ApproachApplication of non-destructive sensors opens a promising opportunity to provide efficient information on nutrient contents based on leaf or canopy reflectance in different crops. In potatoes, nutrient levels are estimated by conducting chemical tests for the petioles. In thinking of deploying sensors for potato nutrient estimation, it is necessary to study the spectrum based on petiole chemical testing rather than leaf chemical testing. Thus, this study aimed to investigate whether there is a... R. Abukmeil, A. Almallahi |
90. Development of 3D Phenotypic Analysis Technology for Precision Monitoring of StrawberriesStrawberries exhibit high overall production volume but low productivity per unit area, primarily due to diseases that occur during cultivation. These yield losses can be mitigated through precision monitoring technologies based on phenotypic analysis. To enhance monitoring accuracy, 3D phenotyping techniques are essential. This study aims to automate such 3D phenotyping by constructing a 3D segmentation model capable of identifying plant organs. Strawberry plants were imaged from all angles ... M. Yang |
91. Development of a Drone-mounted Device for Aerial Application of Mating Disruption Agents in AgricultureIn recent years, drones have been increasingly adopted to reduce workforce and improve the efficiency of pest control in agriculture. However, most drones are optimized for spraying low-viscosity liquid pesticides and thus have limitations in stably applying high-viscosity liquid or solid formulations. In particular, the mating disruption agent (MDA) used in this study, which contains pheromones, must be attached to the crown to maximize pheromone diffusion. It is necessary to develop a techn... S. Song |
92. Development of a Granular Herbicide Spot Applicator for Management of Hair Fescue (Festuca Filiformis) in Wild Blueberry (Vaccinium Angustifolium)Hair fescue has quickly become the pest of greatest concern for the wild blueberry industry. This is largely due to its ability to outcompete wild blueberry for critical resources including water, nutrients and most importantly space. In Nova Scotia, between 2001 and 2019, hair fescue had increased in field frequency from 7% to 68% and in field uniformity from 1.4% to 25%. This rapidly spreading and economically destructive weed is likewise a significant challenge to manage, with only a s... C. Maceachern, T. Esau, Q. Zaman |
93. Development of a Lorawan Wireless Node for Monitoring Smart GreenhousesThe adoption of Internet of Things (IoT) technologies in the smart greenhouse domain is rapidly advancing. Greenhouse planting improves quality and yield by controlling factors affecting crop production. Temperature, humidity, and light intensity in greenhouses are important factors affecting crops. Monitoring and regulating these parameters is conducive to improving the quality and yield of crops. Traditional greenhouse monitoring systems that use wired connections often have problems with c... S. Chung |
94. Development of a Low-power Wireless Communication System Using Lora for Structural Monitoring in Greenhouse FoundationsPlastic greenhouses dominate protected cultivation in South Korea but are vulnerable to extreme weather and foundation instability. To address this issue, a low-power, low-cost monitoring system was developed to estimate foundation attitude and detect anomalies such as uplift. The system integrates an IMU (Inertial Measurement Unit)-based sensor node, LoRa (Long Range) communication, and a gateway in a star topology. Field tests, including pipe uplift and natural conditions, confirmed compara... J. Park |
95. Development of a Measurement and Analysis System for Tillage Operations in Paddy FieldsThis study developed a foundational technology for real-time tillage depth measurement using Inertial Measurement Units (IMUs). The ultimate goal is to enable variable-rate tillage operations tailored to spatial variations in topsoil depth. The system consisted of an RTK-GNSS module and two IMUs to measure the respective pitch angles of the tractor and implement. Tillage depth was estimated using a model derived from the geometric relationship between the implement’s pitch angle and its... E. Morimoto |
96. Development of a Mobile Inspection Robot for Stacked-cage Layers Houses in TaiwanIn stacked-cage layers houses, it is essential to know the eggs produced in each cage per day and their distributions for evaluating egg-laying performance and the health status of the layers. A two-wheel-drive mobile inspection robot for egg-counting was thus designed, assembled, and on-site experiments were performed and evaluated in this paper. The path of the mobile robot was pre-designated according to the site floor layout, so the robot can move autonomously aisle by aisle. Multiple cam... A. Cherng |
97. Development of a Small-Scale Weeding Robot for Inter-Plant Areas Using Vision and Rake MechanismIn low-herbicide or herbicide-free farming systems such as those used for medicinal and herbal crops, weed management remains one of the most labor-intensive tasks. Intra-row weeds, which grow between closely spaced crop plants, are particularly difficult to remove using traditional mechanical methods. Manual weeding, although effective, still poses a significant labor burden and limits the scalability despite the high market value of the crops. To address this challenge, we have de... K. Imaoka |
98. Development of a Small-scale Weeding Robot for Inter-plant Areas Using Vision and Rake MechanismIn low-herbicide or herbicide-free farming systems such as those used for medicinal and herbal crops, weed management remains one of the most labor-intensive tasks. Intra-row weeds, which grow between closely spaced crop plants, are particularly difficult to remove using traditional mechanical methods. Manual weeding, although effective, still poses a significant labor burden and limits the scalability despite the high market value of the crops. To address this challenge, we have de... K. Imaoka |
99. Development of a Smart Agriculture Platform for Modern Management of Longan OrchardsSmart agriculture has emerged as a critical approach in modern agricultural systems. This study aimed to develop a smart agriculture platform for longan orchards by integrating Internet of Things (IoT) technologies and digital systems for precision farming. The study population comprised 100 large-scale agricultural producers located in the provinces of Chiang Mai and Lamphun, Thailand. The developed platform incorporated six core technologies: IoT-based smart irrigation, weather monitoring, ... C. Kanjanaphachoat |
100. Development of a Smart Low-Carbon Greenhouse Integrated Plasma- Activated Water and Second-Life EV BatteriesGlobal warming and the excessive use of nitrogen fertilizers pose significant challenges to sustainable agriculture. This study presents a smart low-carbon greenhouse system powered by a solar photovoltaic microgrid, integrated with second-life electric vehicle batteries and a real-time energy management system. Plasma-activated water (PAW) technology is employed to reduce dependence on chemical nitrogen fertilizers while enhancing crop productivity and reducing nitrous oxide emissions. The s... W. Sean |
101. Development of Ai-based Energy Management Strategy in Seawater Desalination Plant Based on Physical ModelingGlobal water scarcity is becoming increasingly severe, and seawater reverse osmosis (SWRO) has become a major technology for freshwater production due to its high efficiency. However, membrane fouling during long-term operation increases transmembrane pressure, reduces flux, and raises energy demand, ultimately lowering efficiency and shortening membrane lifetime. Traditional control and prediction methods struggle with the nonlinear and dynamic nature of these processes. To address this, we ... W. Sean |
102. Development of an Autonomous Navigation and Obstacle Avoidance Robot for Poultry ShedsTraditional poultry sheds in Taiwan are mostly open-structured, resulting in low efficiency for manual inspection and egg collection, and increased risks of labor fatigue and injuries. This study develops an autonomous obstacle avoidance system for unmanned agricultural vehicles tailored to poultry shed environments, focusing on dynamic path planning and obstacle evasion for safe, efficient navigation.A tracked vehicle chassis enhances stability and adaptability on soft litter floors and narr... J. Chang |
103. Development of an Electric-assisted Handling System for Pig FarmIn swine farming, manure management is a critical yet labor-intensive task. With increasing agricultural labor shortages, optimizing farm infrastructure to reduce manual workload has become essential. Many pig farms in Taiwan utilize elevated slatted floors (concrete or cast iron) to separate pigs from their waste, allowing excrement to fall through gaps for later disposal. While this design improves hygiene by reducing direct contact, the heavy and bulky slatted panels pose significant chall... W. Chen |
104. Development of an In-line Sc-ise Sensor System for Closed Hydroponic Nutrient MonitoringNutrient monitoring is crucial in closed hydroponic systems, where accurate control over individual ion concentrations directly influences crop yield and fertilizer efficiency. While electrical conductivity (EC) sensors are commonly used, they only measure total ionic strength and cannot distinguish between specific ions. Liquid-contact ion-selective electrodes (LC-ISEs) have been studied as an alternative but tend to suffer from durability issues and signal instability under the high-flow co... Y. Jang, W. Cho |
105. Development of an Integrated Harvesting Machine for Taro FieldsTaiwan cultivates a diverse range of agricultural products, among which taro (Colocasia esculenta) is an important root vegetable. Although several harvesters exist for root crops, their applicability remains limited due to crop-specific requirements, and no dedicated integrated harvesting machine is currently available for taro in Taiwan. Farmers still rely heavily on manual labor, using knives or spades to loosen the soil around taro plants before uprooting them individually—a time-co... W. Chen |
106. Development of Automated Rose Monitoring System with Deep Learning-based Growth Stage ClassificationIn cut-flower cultivation, effective production planning is essential to accommodate seasonal fluctuations in market demand. Precise rose growth stage monitoring is critical for cultivation schedule, environmental control, and harvest timing, yet current practices rely on manual observations, which are time-consuming and prone to subjectivity, limiting consistency and scalability. This study presents an automated monitoring system integrating computer vision and deep learning for ob... S. Chen |
107. Development of Cultivar-optimized Nir Spectroscopy Model for Cherry Tomato Maturity and Sweetness Assessment"Yunu" cherry tomato cultivars hold substantial commercial value in Taiwan’s premium markets, where sweetness serves as a key quality attribute. To enhance cultivar-specific quality assessment, this study evaluates tomato quality in both pre-harvest and post-harvest stages.In the pre-harvest stage, image data were used to establish a Red Ripeness Index (RRI) for evaluating tomato maturity. Color calibration techniques were applied to improve consistency, and the stability and ... S. Chen |
108. Development of Efficient Co2 Enrichment Technique Based on a Simple Photosynthesis Model of StrawberriesIn Japanese strawberry production, environmental control in greenhouses is carried out to increase yields and improve fruit quality. CO2 enrichment technique, which promotes leaf photosynthesis by supplying CO2 gas generated by burning kerosene inside greenhouses, has become an indispensable technique in strawberry cultivation. However, conventional CO2 enrichment involves continuous supplementation over a long period of time regardless of the photosynthetic response of strawbe... K. Hidaka |
109. Development of Light-Normalized Crop Monitoring Framework Using RGB-D Imaging and Spatial Light RegressionTo achieve high-quality, high-yield crop production, non-destructive precision monitoring technologies combined with image-based artificial intelligence are being studied to establish finely controlled cultivation environments tailored to crop growth stages. However, variations in lighting-one of the most critical cultivation factors-can cause significant fluctuations in crop image data, limiting the accuracy of phenotype extraction. This study aims to develop a light-normalized crop monitori... M. Yang |
110. Development of Methane Monitoring System for Dairy Cow EructationMethane emissions from dairy cow eructation constituted a significant greenhouse gas source in livestock production and were closely linked to rumination activity. Accurate, continuous, and non-invasive monitoring of eructation events was proven essential for assessing animal health, optimizing feed strategies, and reducing environmental impact. Conventional approaches— including manual observation, jaw-movement sensors, and respiration chambers—remained costly, labor-intensive, a... C. Wu |
111. Development of Rgb and Lidar Fusion Based Pear Fruit Quantification and Mapping SystemThis study presents a system for accurate fruit quantification using LiDAR-RGB sensor fusion. The system projects 2D fruit detections from a YOLO model onto a 3D map generated via SLAM, assigning a unique coordinate to each fruit to prevent double-counting. This approach achieved an aggregate accuracy of 98.5%, with a predicted total of 535 fruits compared to the 527 observed. The resulting data revealed significant fruit density variations (3.2 to 12.6 fruits/m²), establishing the syste... E. Morimoto |
112. Development of Temperature and Humidity Sensor Calibration Procedure for Multifunctional Orchid Greenhouse Monitoring SystemBacterial soft rot and bacterial brown spot are primary diseases that threaten orchid cultivation, often resulting in substantial economic losses. To address labor shortages and environmental challenges in recent years, the orchid industry is increasingly adopting intelligent disease management systems that combine sensing technologies and data analytics as part of its transformation strategy. The multifunctional monitoring system was developed as an economical, integrating sensors for temper... C. Haung |
113. Development of Vision-guided Autonomous Robot for Phenotypic Monitoring in Tomato BreedingPhenotypic monitoring in crop breeding requires continuous data collection throughout growth cycles, yet traditional manual methods are both labor-intensive and time-consuming. Individual plant tracking over extended periods poses particular challenges due to field scale and measurement frequency requirements across diverse agricultural environments. This study presents an autonomous robotic platform integrating computer vision and precision positioning technologies for automated phenotypic d... S. Chen |
114. Development of Weeding Robots Using Ai Image Recognition TechnologyIn this study, a laser weeding robot was developed to remove weeds, one of the factors that interfere with the growth of crops in agricultural fields. The driving unit controlled the speed of the DC motor using an Arduino-Mega (MCU). The weed recognition unit calculated the location information of the weeds using the cabbage and weed image recognition model (YOLO-v8) mounted on the control device (Jetson-Orin). The calculated location information was transmitted to the MCU through serial comm... H. Kim |
115. Development, Design, and Integration of an Egg Tray System with Unmanned Ground Vehicle for Robotic Poultry AutomationEggs are among the most extensively consumed foods, valued for their nutritional and health benefits. While generally studied, the exact fine representation of a raspberry’s egg shape remains complex. Egg forms are usually classified as globular, ellipsoidal, elliptical, and pyriform- the last of which still lacks a definitive equation. This study presents a new system for modeling egg figures and calculating volume, based on crucial parameters, including the major axis, the m... B. Gonzales |
116. Develpoment of Bagged Guava Quality Grading System Using Image Recognition and Generative Adversarial Networks(GANS)Taiwan’s warm climate and abundant sunlight make it highly suitable for guava cultivation, making guava an important economic crop. However, current quality grading still relies on manual inspection, which is labor-intensive, inconsistent, and affected by bagging practices. To address this, we propose an automatic grading system using deep learning and generative adversarial networks. The framework collects images of bagged and bare guavas, applies YOLOv9 for fruit detection and backgro... C. Chang |
117. Diagnosis of Grapevine Nutrient Content Using Proximal Hyperspectral ImagingNutrient deficiencies on grapevines could affect the fruit yield and quality, which is a major concern in vineyards. Nutrient deficiencies may be recognizable by foliar symptoms that vary by mineral nutrient and stress severity, but it is too late to manage when visible deficiency symptoms become apparent. The nutrient analysis in the laboratory is the way to get an accurate result, but it is time and cost-intensive. The differences in leaf nutrient levels also alter spectral characteristics ... C. Kang, M. Karkee, Q. Zhang, N. Shcherbatyuk, P. Davadant, M. Keller |
118. Digital Soil Sensing and Mapping for Crop SuitabilitySoil, central to any land-based production system, determines the success of any crops. While soil for a farm or field is fixed, the crops can be selected to best fit the soil’s capability and production. Traditionally crops are selected based on farm history, knowledge, and years of trial and error to tailor the right crop to the right soil. Inherent challenges associated with this make the whole process unsustainable. Due to the consistent nature of the information collected, soil sen... D. Saurette, A. Biswas, T.B. Gobezie |
119. Disease Symptom Recognition and Severity Assessment for Phalaenopsis OrchidsTraditional disease assessment relies on manual visual inspection, which is subjective and often leads to inconsistent results due to variations in human judgment. To address these challenges, this study proposes an automated approach for disease classification and severity grading in Phalaenopsis orchids using the YOLOv8-seg deep learning model. The system integrates instance segmentation with Lab color space analysis, which was found to outperform HSV in distinguishing healthy and diseased ... C. Huang |
120. Dual-channel Imaging and Two-stage Deep Learning for Fertility Detection of Duck EggsIn Taiwan, the waterfowl industry generates a production value of NT$11.2 billion, of which meat ducks contribute about 80% (≈NT$8.9 billion). As the upstream segment of the duck meat industry, the hatching process of duck eggs plays a critical role in duck production. Fertilized eggs require a clean incubation environment to develop properly. To protect this environment, unfertilized eggs need to be removed at an early stage, which makes fertility detection essential. However, conventi... Y. Kuo |
121. Early Warning System Based on Deep Learning for Multi-type Abnormal Chicken Comb DetectionWith the rapid development of smart agriculture, environmental sensing technologies have been widely applied to enhance production efficiency and management in poultry farming. However, existing poultry house systems mainly focus on monitoring environmental parameters such as temperature, humidity, and gas concentrations, offering limited capabilities for real-time assessment of individual chicken health. Currently, flock health largely relies on manual inspections by farm personnel, which ar... Y. Chu |
122. EarthScout, GBCEarthScout is a precision remote sensor technology that provides farmers and researchers with reliable data in real time, straight from your field to your desktop and mobile devices. In season data allows users to access current conditions for smarter decision making in irrigation and nitrogen management. EarthScout is a crop agnostic tool that is used in any soil type and climate. Our plug and play field sensors need no calibration and set up only takes about 5 minutes. There are no data sub... S. Wieland, A. Kelley |
123. Ecological Refugia As a Precision Conservation Practice in Agricultural SystemsCurrent global agriculture fails to meet the basic food needs of 687.7 million people. At the same time, our food system is responsible for catastrophic losses of biodiversity. Precision conservation solutions offer the potential to benefit both production systems and natural systems. Transforming low-producing areas on farm fields into ecological refugia may provide small-scale habitat and ecosystem services in fragmented agricultural landscapes. We collaborated with three precision agricult... H. Duff, B. Maxwell |
124. Economic Potential of IPMwise – a Generic Decision Support System for Integrated Weed Management in 4 CountriesReducing use and dependency on pesticides in Denmark has been driven by political action plans since the 1980ies, and a series of nationally funded accompanying R&D programs were completed in the period 1989-2006. One result of these programs was a decision support system (DSS) for integrated weed management. The 4th generation (2016) of the agro-biological models and IT-tools in this DSS, named IPMwise. The concept of IPMwise is to systematically exploit that: ... P. Rydahl, O. Boejer, K. Torresen, J.M. Montull, A. Taberner, H. Bückmann, A. Verschwele |
125. Economic Potential of RoboWeedMaps - Use of Deep Learning for Production of Weed Maps and Herbicide Application MapsIn Denmark, a new IPM ‘product chain’ has been constructed, which starts with systematic photographing of fields and ends up with field- or site-specific herbicide application. A special high-speed camera, mounted on an ATV took sufficiently good pictures of small weed plants, while driving up to 50 km/h. Pictures were uploaded to the RoboWeedMaps online platform, where appointed internal- and external persons with agro-botanical experience executed ‘virtual field ... P. Rydahl, O. Boejer, N. Jensen, B. Hartmann, R. Jorgensen, M. Soerensen, P. Andersen, L. Paz, M.B. Nielsen |
126. Economics of Field Size for Autonomous Crop MachinesField size constrains spatial and temporal management of agriculture with implications for farm profitability, field biodiversity and environmental performance. Large, conventional equipment struggles to farm small, irregularly shaped fields efficiently. The study hypothesized that autonomous crop machines would make it possible to farm small non-rectangular fields profitably, thereby preserving field biodiversity and other environmental benefits. Using the experience of the Hands Free Hectar... A. Al amin, J. Lowenberg‑deboer, K. Franklin, K. Behrendt |
127. Edge-AI-based Dairy Calf Behavior Monitoring System Using Computer Vision and Iot TechnologiesWe present an edge-AI, IoT system for real-time monitoring of dairy calf behavior that runs on embedded system and streams only compact results to the cloud. A lightweight, quantized MoViNet-A2 model deployed on a Raspberry Pi 4 classifies seven behaviors (non-active/active lying, non-active/active standing, feeding, drinking, ruminating) from 4-s clips captured once per minute, and publishes JSON outputs to AWS for dashboards. Field trials on three Holstein calves at the National Taiwan Univ... T. Lin |
128. Effectiveness of Different Precision Soil Sampling Strategies for Site-Specific Nutrient Management in Row-CropsSoil sampling is an important component of site-specific nutrient management in precision agriculture. While precision soil sampling strategies such as grid or zone have been around for a while, the adoption and utilization of these strategies varies considerably among the growers, especially in the southeastern United States. The selection of an appropriate grid size or management zone further differ among the users depending on several factors. In order to better understand how some of the ... M.W. Tucker, S. Virk, G. Harris, J. Lessl, M. Levi |
129. Embodied Agentic Artificial Intelligence for Precision Agriculture: Cross-domain Experience from Multimodal Generative AIMy team develops inclusive, responsible, and multimodal AI technology across education, healthcare, and digital services grounded in our research in embodied agentic intelligence and large language models. I will share deployed examples from these domains and draw parallels to agriculture, where similar technical challenges persist, ranging from multimodal fusion for contextual reasoning, explainable AI for actionable insights, and data-efficient learning for adaptation and localization. Whil... N. Chen |
130. Employment of the SSEB and CROPWAT Models to Estimate the Water Footprint of Potato Grown in Hyper-arid Regions of Saudi ArabiaQuantifying crops’ water footprint (WF) is essential for sustainable agriculture especially in arid regions, which suffers from harsh environmental conditions and severe shortage of freshwater resources such as Saudi Arabia. In this study, WF of irrigated potato crop was estimated for the implementation of precision agriculture techniques. The CROPWAT and the Simplified Surface Energy Balance (SSEB) approaches were adopted. Soil, plant, and yield samples were randomly collected from six... R. Madugundu, K. Al-gaadi, E. Tola |
131. Energy Harvesting and Application of a Tubular Triboelectric Nanogenerator Driven by Water FlowIn recent years, growing awareness of sustainable and environmentally friendly practices has driven the development of innovative renewable energy technologies. To mitigate the environmental impact of energy production, this study proposed a tubular Triboelectric Nanogenerator (TENG) capable of continuously and stably harvesting energy from flowing water. The designed tubular TENG consisted of two structural components: the inner part served as the primary power generation unit, comprising a ... P. Liao |
132. Enhancing NY State On-farm Experimentation with Digital AgronomyAgriculture is putting pressure on the ecosystems and practices need to evolve towards a more sustainable way of producing food. Industrial agriculture has imposed a unique production model on the ecosystems while it is now understood that it is more sustainable to adapt the production model to the ecosystem. This involves adapting existing solutions to the local agricultural context and developing new solutions that are best suited to the local ecosystem. Farmers are doing this by conducting... L. Longchamps |
133. Enhancing PA Adoption Through Value ConnectionsDespite an increase in breadth of precision agriculture over time, and the attendant elements of digital agriculture that either support PA or integrates the outputs of PA, the pace of adoption of digital agriculture in our farming systems remains slow. In assessing impediments to adoption of digital agriculture, much work to date has focused on the value proposition as considered by individual producers or value chain actors. At this level, adoption remains constrained by perceptions o... D.W. Lamb, M.T. Schaefer |
134. Enhancing Rice Disease Management: Estimating Pathogen Damage Through Multispectral Imaging AnalysisThis study investigates the application of multispectral imaging (MSI) in conjunction with machine learning algorithms for the early detection and estimation of pathogen damage in rice crops, with a specific focus on Bacterial Leaf Blight (BLB) and Blast diseases. Rice plays a crucial role in global food security, yet these diseases significantly compromise its production. Traditional diagnostic methods are often labor-intensive and time-consuming, necessitating the adoption of innovative tec... I. Sutrisna wijaya |
135. Enhancing Spatial Resolution of Maize Grain Yield DataGrain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops. Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used t... J. Siegfried, R. Khosla, D. Mandal, W. Yilma |
136. Enhancing Sustainable Farming of Nh: Mechanization of Planting and Post Harvest CleaningNymphoides hydrophylla (NH), commonly known as white water snowflake, is a culturally and nutritionally important aquatic vegetable, particularly valued in Taiwan's Hakka communities. However, its commercial scalability remains limited due to labor-intensive practices in both planting and post-harvest cleaning. This study introduces an integrated mechanized system that combines a seedling planting tool and a cleaning machine, designed to enhance overall production efficiency, reduce ... W. Lin |
137. Establishing the First Soil Water Characteristics Curve for the Soils of Prince Edward Island, CanadaSoil water characteristics curve (SWCC), for Prince Edward Island (PEI), is much more needed currently for the sustainable production of agriculture yields. It will not only fulfil the requirements of the province’s farmers for irrigation scheduling but also help the government to decide about permitting the use of groundwater for supplemental irrigation on the island. A soil water characteristics curve in PEI does not exist to support precision agriculture practices. Precision ir... S.J. Cheema, A.A. Farooque, F. Abbas, T. Esau, K. Grewal |
138. Establishment of a Canola Emergence Assessment Methodology Using Image-based Plant Count and Ground Cover AnalysisManual assessment of emergence is a time-consuming practice that must occur within a short time-frame of the emergence stage in canola (Brassica napus). Unmanned aerial vehicles (UAV) may allow for a more thorough assessment of canola emergence by covering a wider scope of the field and in a more timely manner than in-person evaluations. This research aims to calibrate the relationship between emerging plant population count and the ground cover. The field trial took place at the Uni... K. Krys, S. Shirtliffe, H. Duddu, T. Ha, A. Attanayake, E. Johnson, E. Andvaag, I. Stavness |
139. Establishment of Spatial Information for Soybean Cultivation Complex Through Drone Image AnalysisThis study demonstrates that time-series drone imagery can effectively monitor crop growth in large-scale soybean paddy complexes. Additionally, spatial data were constructed for each field, including geographic coordinates, parcel numbers, area, crop type, sowing date, and growth information. ... J. Park |
140. Estimating Rice Canopy Height Using a Ground-based Slam Lidar SystemThis study evaluates the application of a ground-based LiDAR system, integrated with a Simultaneous Localization and Mapping (SLAM) algorithm, to estimate rice crop canopy height (CH). Using the Velodyne VLP-16 LiDAR sensor, point cloud data were collected and processed to map the rice field. The experimental area covered approximately 600 m² during the crop’s vegetative stage. LiDAR-derived canopy height (LCH) was extracted using percentile-based metrics and compared with manual m... E. Morimoto |
141. Estimating Soil Carbon Stocks with In-field Visible and Near-infrared SpectroscopyAgricultural lands can be a sink for carbon and play an important role in offsetting carbon emissions. Current methods of measuring carbon sequestration—through repeated temporal soil samples—are costly and laborious. A promising alternative is using visible, near-infrared (VNIR) diffuse reflectance spectroscopy. However, VNIR data are complex, which requires several data processing steps and often yields inconsistent results, especially when using in situ VNIR measurements. Using... C.J. Ransom, C. Vong, K.S. Veum, K.A. Sudduth, N.R. Kitchen, J. Zhou |
142. Estimation of Cotton Biomass Using Unmanned Aerial Systems and Satellite-based Remote SensingSatellite and unmanned aerial system (UAS) images are effective in monitoring crop growth at various spatial, temporal, and spectral scales. The objective of the study was to estimate cotton biomass at different growth stages using vegetation indices (VIs) derived from UAS and satellite images. This research was conducted in a cotton field in Hale County, Texas, in 2021. Data collected include 54 plant samples at different locations for three dates of the growing season. Multispectral images ... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo |
143. Estimation of Crop Coefficient in Malaysian Durian Using Satellite Data and Machine LearningDurian (Durio zibethinus) is a popular fruit and key crop in Southeast Asia, known as the “King of Fruits” for its thorny exterior and distinctive aroma. The crop coefficient (Kc), based on crop evapotranspiration (ETc) and reference evapotranspiration (ETo), is crucial for water efficiency. Currently, there is no Kc value for Malaysian durian. This study introduces a machine learning method utilizing remote sensing data from Sentinel-1, Sentinel-3, and MODIS ET, combined wit... S.K. Balasundram |
144. Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and IndianaPrecision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minne... K. Mizuta, Y. Miao, A.C. Morales, L.N. Lacerda, D. Cammarano, R.L. Nielsen, R. Gunzenhauser, K. Kuehner, S. Wakahara, J.A. Coulter, D.J. Mulla, D. . Quinn, B. Mcartor |
145. Evaluating APSIM Model for Site-Specific N Management in NebraskaMany approaches have been developed to estimate the optimal N application rates and increase nitrogen use efficiency (NUE). In particular, in-season and variable-rate fertilizer applications have the potential to apply N during the time of rapid plant N uptake and at the rate needed, thereby reducing the potential for nitrogen fertilizer losses. However, there remains great challenges in determining the optimal N rate to apply in site-specific locations within a field in a given year.&nb... L. Thompson, L. Puntel, S. Archontoulis |
146. Evaluating Flight Path Strategy for Uav-based Phenotyping of Individual Muskmelon Plant in Greenhouse EnvironmentsUnmanned Aerial Vehicle (UAV)-based phenotyping is an emerging non-invasive method for high-throughput trait measurement in controlled environments. This study examines how UAV flight trajectory affects reconstruction fidelity and trait accuracy for muskmelon and grape plants in a GPS-denied greenhouse. Two strategies - circular loop and vertical hop - were flown using a UAV with RGB-D SLAM navigation, capturing data with a RunCam Thumb Pro. Data were processed through a GLOMAP structure-from... T. Lin |
147. Evaluating How Operator Experience Level Affects Efficiency Gains for Precision Agricultural ToolsTractor guidance (TG) improve environmental gains relative to non-precision technologies; however, studies evaluating how tractor operator experience for non-guidance comparisons impact gains are nonexistent. This study explores spatial relationships of overlaps and gaps with operator experience level (0-1; 2-3; 6+ years) during fertilizer and herbicide applications based on terrain attributes. Tractor paths recorded by global navigation satellite systems were used to create overlap pol... A. Ashworth, T. Kharel, P. Owens |
148. Evaluating the Potential of Improving In-season Nitrogen Status Diagnosis of Potato Using Leaf Fluorescence Sensors and Machine LearningPrecision nitrogen (N) management is particularly important for potato crops due to their high N fertilizer demand and high N leaching potential caused by their shallow root systems and preference for coarse-textured soils. Potato farmers have been using a standard lab analysis called petiole nitrate-N (PNN) test as a tool to diagnose potato N status and guide in-season N management. However, the PNN test suffers from many disadvantages including time constraints, labor, and cost of analysis.... S. Wakahara, Y. Miao, S. Gupta, C. Rosen, K. Mizuta, J. Zhang, D. Li |
149. Evaluating the Potential of Integrated Precision Irrigation and Nitrogen Management for Corn in MinnesotaThe environmental impact of irrigated agriculture on ground and surface water resources in Minnesota is of major concern. Previous studies have focused on either precision irrigation or precision nitrogen (N) management, with very limited studies on the integrated precision management of irrigation and N fertilizers, especially in Minnesota. The Dualex Scientific sensor is a leaf fluorescence sensor that has been used to diagnose crop&nbs... A. Elvir flores, Y. Miao, V. Sharma, L. Lacerda |
150. Evaluation of Crop Model Based Tools for Corn Site-specific N Management in NebraskaThere is a critical need to reduce the nitrogen (N) footprint from corn-based cropping systems while maintaining or increasing yields and profits. Digital agriculture technologies for site-specific N management have been demonstrated to improve nitrogen use efficiency (NUE). However, adoption of these technologies remains low. Factors such as cost, complexity, unknown impact and large data inputs are associated with low adoption. Grower’s hands-on experience coupled with targeted resear... L. Puntel, L. Thompson , T. Mieno, S. Norquest |
151. Evaluation of High-throughput 3d Reconstruction Method for Plants and Its Application to Traits Feature Extraction2D images are widely utilized to monitor and evaluate plant growth, capturing the dynamic and multi-directional nature of plant canopies remains difficult, emphasizing the need for 3D monitoring integrated with plant phenotyping systems.This study aims to introduce a high-throughput plant phenotyping system using 3D plant shape model reconstructed from a dataset of 2D plant images from multiple camera poses. A robot autonomously gathered data by recording video footage of plants from various ... T. Okayasu |
152. Evaluation of Image Acquisition Parameters and Data Extraction Methods on Plant Height Estimation with UAS ImageryAerial imagery from unmanned aircraft systems (UASs) has been increasingly used for field phenotyping and precision agriculture. Plant height is one important crop growth parameter that has been estimated from 3D point clouds and digital surface models (DSMs) derived from UAS-based aerial imagery. However, many factors can affect the accuracy of aerial plant height estimation. This study examined the effects of image overlap, pixel resolution, and data extraction methods on estimati... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster |
153. Evaluation of Indwelling Rumen Temperature Monitoring System for Dairy Calf Illness Detection and ManagementPrecision Dairy Farming technology has mostly focused on tools to improve cow care, but new tools are available to improve the care of pre-wean calves and heifers. These technologies apply real-time monitoring to measure individual animal data and detect a deviation from normal. On-farm validation of new technologies remains important for successful deployment of new technologies within commercial farms to understand how the technology can improve dairy calf welfare, performance, and health. ... J.M. Hartschuh, J.P. Fulton, S.A. Shearer, B.D. Enger, G.M. Schuenemann |
154. Evaluation of Nitrogen Recommendation Tools for Winter Wheat in NebraskaAttaining both high yield and high nitrogen (N) use efficiency (NUE) simultaneously remains a current research challenge in crop production. Digital ag technologies for site-specific N management have been demonstrated to improve NUE. This is due to the ability of digital technologies to account for the spatial and temporal distribution of crop N demand and available soil N in the field which varies greatly according t... J. Cesario pereira pinto, L. Thompson, N. Mueller, T. Mieno, G. Balboa, L. Puntel |
155. Evaluation of Planting Accuracy and Early Growth Uniformity of Spring Cabbage in GreenhousesMechanized transplanting reduces labor and time in greenhouse cabbage production, yet misplacement, over burial, and missing seedlings still compromise uniform stands This study evaluated transplant quality and early growth uniformity with two stages during transplanting and harvesting image and machine learning workflow at plot scale. Two transplanters, automatic and semi-automatic, were tested under ridge widths of 60, 70 and 80 cm and seedling ages of 30 and 35 days. In February after tran... S. Chung |
156. Evaluation of Unmanned Aerial Vehicle Images in Estimating Cotton Nitrogen ContentEstimating crop nitrogen content is a critical step for optimizing nitrogen fertilizer application. The objective of this study was to evaluate the application of UAV images in estimating cotton (Gossypium hirsutum L.) N content. This study was conducted in a dryland cotton field in Garza County, Texas, in 2020. The experiment was implemented as a randomized complete block design with three N rates of 0, 34, and 67 kg N ha-1. A RedEdge multispectral sensor was used to acqu... R. Karn, H. Gu, O. Adedeji, W. Guo |
157. Fabrication of Laser-induced Graphene Heater Integrated into Flexible Printed Circuit BoardsFlexible Printed Circuit Board (FPCB) are widely used in portable devices, wearable systems, and biomedical sensing applications because their flexibility, thinness, and high integration. With the development of the Internet of Things and smart agriculture, sensors are evolving toward lightweight and multifunctional designs, including applications in temperature and humidity monitoring, pressure sensing, physiological signal acquisition, and gas detection. Most gas sensors require high-temper... C. Wang |
158. Farmer Charlie - Low Cost Data Analytics for Farmers Accessible in the FieldFarmer Charlie, a spin-off of AB5 Consulting Ltd, is based on an affordable business model including five elements: a data analytics platform, an agribusiness ecosystem app, capable of connecting with local third-party apps; weather and in field sensors; wi-fi Internet connectivity; and power to the field and farms via solar panels, where necessary. Farmer Charlie brings information to farmers in their own fields, in an easy plug and play solution, affordable to the farmers and addressing the... B. Bonnardel |
159. Farmer Charlie - Low Cost Smart Local Data Available to Remote FarmersFarmer Charlie brings connectivity and information to farmers, who receive tailored agronomic data to improve their agricultural practice. Farmer Charlie is based on on-site sensors through which soil data can be detected, gathered, and processed by a dedicated server. Broadband communication allows farmers to receive real-time, localised information on tablet or mobile phone. Farmer Charlie is a low-cost solution, it can be adapted to various crops and to detect soil humidity, pH, temperatur... B. Bonnardel |
160. Farmers’ and Experts’ Perceptions of Precision Farming Impacts on Economic Efficiency, Food Security, Climate and Environmental Sustainability“Global food security could be in jeopardy, due to mounting pressures on natural resources and to climate change, both of which threaten the sustainability of food systems at large. Excessive fertilizer use can contribute to problems of eutrophication, acidification, climate change and the toxic contamination of soil, water and air. Lack of fertilizer application may cause the degradation of soil fertility. Agricultural production systems need to focus more on the effective co... C.I. Anaba |
161. Field Testing of a Laboratory-made Portable Hydroponic Nutrient Analyzer with Ion-selective ElectrodesAs a strategy to address climate change and declining agricultural productivity, hydroponic systems have gained increasing attention. In particular, precise control of nutrient ion composition in nutrient solutions is essential for ensuring stable crop growth and improving product quality. However, most hydroponic farms currently rely on pH and electrical conductivity (EC) sensors for nutrient solution management. While EC reflects the overall ionic strength, it does not provide quantitative ... H. Kim |
162. From Fragmented Data to Unified Insights: Leveraging Data Standardization Tools for Better Collaboration and Agronomic Big Data AnalysisThe quantity and scope of agronomic data available for researchers in both industry and academia is increasing rapidly. Data sources include a myriad of different streams, such as field experiments, sensors, climatic data, socioeconomic data or remote sensing. The lack of standards and workflows frequently leads agronomic data to be fragmented and siloed, hampering collaboration efforts within research labs, university departments, or research institutes. Researchers and businesses therefore ... S. Sela |
163. Fruit Fly Electronic Monitoring SystemInsects are a constant threat to agriculture, especially the cultivation of various types of fruits such as apples, pears, guava, etc. In this sense, it is worth mentioning the Anastrepha genus flies (known as fruit fly), responsible for billionaire losses in the fruit growing sector around the world, due to the severity of their attack on orchards. In Brazil, this type of pests has been controlled in most product areas by spraying insecticides, which due to the need for prior knowledge regar... C.L. Bazzi, F.V. Silva, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, R.S. Dos santos, A.M. Hachisuca, F. Franz |
164. Functional Soil Property Mapping with Electrical Conductivity, Spectral and Satellite Remote SensorsProximal electrical conductivity (EC) and spectral sensing has been widely used as a cost-effective tool for soil mapping at field scale. The traditional method of calibrating proximal sensors for functional soil property prediction (e.g., soil organic matter, sand, silt, and clay contents) requires the local soil sample data, which results in a field-specific calibration. In this large-scale study consisting of 126 fields, we found that the traditional local calibration method had suffered w... X. Xiong, D. Myers, J. Debruin, B. Gunzenhauser, N. Sampath, D. Ye, H. Underwood, R. Hensley |
165. Fusing Deep Learning and Control Theory for Optimized Sugar Beet Yield PredictionAccurate yield prediction is a vital field of research in precision agriculture, enabling optimal resource allocation and enhanced food security under growing climatic uncertainty. Traditional models struggle to capture complex, non-linear interactions between environmental drivers and crop growth. To address this, we present our approach, a multi-stage method for sugar beet yield prediction and management that integrates deep learning with control-theoretic techniques and mathematical langua... A. Tabbassi |
166. Gamma-ray Spectrometry to Determine Soil Properties for Soil Mapping in Precision AgricultureSoil maps are critical for various land use applications and form the basis for the successful implementation of precision agriculture in crop production. Soil maps provide the spatial distribution of important soil physical and chemical properties to a farmer. The farmer uses this information to make critical management decisions for profitable and sustainable food production. South Africa is a water scarce country where rainfall is mainly seasonal and unreliable. Under these circumstances, ... J.G. Dreyer, L. Ameglio |
167. Generation of Site-specific Nitrogen Response Curves for Winter Wheat Using Deep LearningNitrogen response (N-response) curves are tools used to support farm management decisions. Conventionally, the N-response curve is modeled as an exponential function that aims to identify an important threshold for a given field: the economic optimum point. This is useful to determine the nitrogen rate beyond which there is no actual profit for the farmers. In this work, we show that N-response curves are not only field-specific but also site-specific and, as such, economic optimum points sho... G. Morales, J.W. Sheppard, A. Peerlinck, P. Hegedus, B. Maxwell |
168. Grassland System Impacts on Spatial Variability of Soil Phosphorus in Eastern CanadaPhosphorus (P) is an essential nutrient for plants, including grasslands. However, continuous applications of P fertilizer result in P accumulations in the soil, increasing the risk of P losses through runoff and erosion. Since 2008, more than 31 million tonnes of organic fertilizers, representing more than 95,000 tonnes of P2O5, were applied to agricultural fields in Eastern Canada. Thus, grassland systems were fertilized intensively using organic fertilizers with high ... J.D. Nze memiaghe, A. Cambouris |
169. Hay Yield Estimation Using UAV-based Imagery and a Convolutional Neural NetworkYield monitoring systems are widely used commercially in grain crops to map yields at a scale of a few meters. However, such high-resolution yield monitoring and mapping for hay and forage crops has not been commercialized. Most commercial hay yield monitoring systems only obtain the weight of individual bales, making it difficult to map and understand the spatial variability in hay yield. This study investigated the feasibility of an unmanned aerial vehicle (UAV)-based remote sensing system ... K. Lee, K.A. Sudduth, J. Zhou |
170. High-reliability Navigation for Multi-functional Robots Using Rfid Triggers and 3d Slam in a Protected HorticultureProtected horticulture in Japan is facing a serious labor shortage, yet existing robots have not achieved sufficient return on investment, and their adoption remains limited. To support the deployment of multi-functional robots, we developed a high-reliability autonomous navigation system that integrates RFID-based event-triggered state transitions with LiDAR-based simultaneous localization and mapping (SLAM).The developed mobile platform was built on an omnidirectional robot equipped with fo... T. Okayasu |
171. HMI-integrated Environmental Sensing for Poultry Water-intake ForecastEnvironmental monitoring is crucial in poultry farming, yet traditional reliance on manual inspection is often labor-intensive and inefficient. This study addresses these challenges by developing a comprehensive poultry production management system. The research utilized environmental data—specifically temperature, humidity, carbon dioxide (CO₂), and ammonia (NH₃)—from a guinea fowl population at the Biaoyu Husbandry Farm in Miaoli County. The core of the system is a Human-Mac... H. Chen |
172. How Digital is Agriculture in South America? Adoption and LimitationsA rapidly growing population in a context of land and water scarcity, and climate change has driven an increase in healthy, nutritious, and affordable food demand while maintaining the current cropping area. Digital agriculture (DA) can contribute solutions to meet the demands in an efficient and sustainable way. South America (SA) is one of the main grain and protein producers in the world but the status of DA in the region is unknown. This article presents the results from a systematic revi... G. Balboa, L. Puntel, R. Melchiori, R. Ortega, G. Tiscornia, E. Bolfe, A. Roel, F. Scaramuzza, S. Best, A. Berger, D. Hansel, D. Palacios |
173. How Do Different Data Intervals in Exponential Sine Model Affect Prediction of Strawberry Flowering Dynamics?In strawberry cultivation, where harvests occur periodically, predicting flowering dynamics is crucial for optimizing yield. This study aimed to organize fundamental information on the exponential sine model, which could play a central role in developing prediction processes for strawberry flowering dynamics. To achieve this, the model was applied to flowering data, and the impacts of different data intervals on predictive performance (trends and accuracy) were evaluate. Over three cultivatin... S. Ono |
174. Identification of Citrus Diseases, Pests, and Disorders Using Deep LearningTaiwan’s warm climate offers favorable conditions for citrus production, making it the most economically valuable fruit crop in the country. Citrus trees are perennial and mainly propagated asexually. Long-term exposure and limited genetic diversity make them more susceptible to infection by various pathogens. In practice, diagnosis often relies on farmers’ experience, which can be subjective despite their familiarity with local conditions. Microscopic examination by plant patholo... Y. Kuo |
175. Identification of Cucumber Pests, Diseases, and Disorders Using Deep LearningCucumber is an essential economic crop worldwide, which is typically cultivated in summer. The hot and humid conditions make them highly susceptible to various pests, diseases, and physiological disorders, which hinder their growth and lead to significant yield losses. Early and accurate detection is vital to limiting the spread of diseases or pests. However, traditional diagnostic approaches rely heavily on visual inspection by experienced farmers or microscopic examination by specialists, w... Y. Kuo |
176. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone DelineationManagement zone delineation is a practical strategy for site-specific management. Numerous approaches have been used to identify these homogenous areas in the field, including approaches using multiple years of historical yield maps. However, there are still knowledge gaps in identifying variables influencing spatial and temporal variability of crop yield that should be used for management zone delineation. The objective of this study is to identify key soil and landscape properties affecting... L.N. Lacerda, Y. Miao, K. Mizuta, K. Stueve |
177. Impact of Cover Crop and Soil Apparent Electrical Conductivity on Cotton Development and YieldCotton is one of the major crops in the New Madrid Seismic Zone (NMSZ) of the U.S. Lower Mississippi River Valley region. Because cotton production doesn’t leave a lot of crop residue in the field, low soil organic matter levels are common. While the benefits of crop rotation are well known, cotton is often grown year after year in the same fields for economic reasons. Soils in the region are generally quite variable, with areas of very high sand content. Winter cover crops and reduced ... E. Vories, K. Veum, K. Sudduth |
178. Impacts of Interpolating Methods on Soil Agri-environmental Phosphorus Maps Under Corn ProductionPhosphorus (P) is an essential nutrient for crops production including corn. However, the excessive P application, tends to P accumulation at the soil surface under crops systems. This may contribute to increase water and groundwater pollution by surface runoff. To prevent this, an agri-environmental P index, (P/Al)M3, was developed in Eastern Canada and USA. This index aims to estimate soil P saturation for accurate P fertilizer recommendations, while integrating agronomical aspec... J. Nze memiaghe, A.N. Cambouris, N. Ziadi, M. Duchemin, A. Karam |
179. Improving Depth Accuracy by Using a Real-time Monitoring System for Traditional Tillage MachineryTillage depth has a great influence on soil quality, fuel consumption, and equipment durability in mechanized farming. However, traditional methods often maintain a fixed depth, lacking the ability to adjust in real time. This study proposes a real-time monitoring system that significantly improves the depth measurement accuracy of traditional tillage machinery. The system is equipped with a soil contact wheel combined with an angle sensor, which converts the rotation angle into a depth value... W. Lin |
180. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather DataNitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by c... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia |
181. In-season Nitrogen Management of Maize Based on Nitrogen Status and Lodging Risk PredictionDevelopment of effective precision nitrogen (N) management strategies is crucially important for food security and sustainable development. Lodging is one of the major constraints to increasing maize yield that can be induced by strong winds, and is also influenced by management practices, like N rate. When making in-season N application decisions, lodging risk should be considered to avoid yield loss. Little has been reported on in-season N management strategies that also incorporate lodging... R. Dong, Y. Miao, X. Wang |
182. Increasing Precision Irrigation Efficacy for Row Crop Agriculture Through the Use of Artificial IntelligenceThe agricultural sector is the largest consumer of the world’s available fresh water resources. With fresh water scarcity increasing worldwide, more efficient use for irrigation water is necessary. Precision irrigation is described as the application of water to meet crop needs of a specific area, at the right amount and at the time that is optimum for crop health and management objectives. Irrigation becomes increasingly efficient through the use of precision irrigation tools. Howe... E. Bedwell |
183. Increasing the Accuracy of UAV-Based Remote Sensing Data for Strawberry Nitrogen and Water Stress DetectionThis paper presents the methods to increase the accuracy of unmanned aerial vehicles (UAV)-based remote sensing data for the determination of plant nitrogen and water stresses with increased accuracy. As the demand for agricultural products is significantly increasing to keep up with the growing population, it is important to investigate methods to reduce the use of water and chemicals for water conservation, reduction in the production cost, and reduction in environmental impact. UAV-based r... S. Bhandari, A. Raheja |
184. Innovating Irrigation: Affordable Smart Solutions for Water SustainabilityAgriculture accounts for 70–80% of global freshwater use, a level increasingly unsustainable under climate change. This study reports the development and field validation of a low-cost smart irrigation system for tomato and melon in Tuscany (2021–2023). The system integrates evapotranspiration-based models, wireless sensor networks, and adaptive control algorithms. In 2023 it achieved up to 50% water savings compared to traditional practices, without yield reduction, at a total co... A. Matese |
185. Integration of a Real-time Dairy Cow Eye Temperature Monitoring System Based on Deep Learning and Thermal ImagingEarly detection of heat stress and illness in dairy cows is critical for maintaining herd health and optimizing milk production. Among various physiological signals, body temperature is a key indicator of health status. In this study, we present a real-time, non-contact monitoring system that integrates dual-channel thermal imaging and deep learning for precise and automated surveillance. The system processes RGB and thermal video streams in parallel: in the RGB channel, YOLO detects faces, B... T. Lin |
186. Integration of Unmanned Aerial Systems Images and Yield Monitor in Improving Cotton Yield EstimationThe yield monitor is one of the most adopted precision agriculture technologies because it generates dense yield data to quantify the spatial variability of crop yield as a basis for site-specific management. However, yield monitor data has various errors that prevent proper interpretation and precise field management. The objective of this study was to evaluate the application of unmanned aerial systems (UAS) images in improving cotton yield monitor data. The study was conducted in a dryland... H. Gu, W. Guo |
187. Intra-row Mechanical Cabbage Weeding Based on Machine VisionCabbage production is strongly influenced by environmental factors such as weather, soil, weeds, and pests, which can reduce both yield and quality. Chemical weeding is efficient and inexpensive but restricted due to environmental and food safety concerns, while manual weeding is safe yet labor-intensive. To address these issues, this study proposes a machine vision–based in-row weeding system that integrates a belt-driven sliding module with an embedded computing platform. Using the YO... H. Lin |
188. Investigating Spatial Relationship of Apparent Electrical Conductivity with Turfgrass and Soil Characteristics in Sand-capped Golf Course FairwaysTurfgrass quality decreases when grown on fine textured soils that are irrigated with poor quality water. As a result, sand-capping (i.e., a sand layer above existing native soil) is now considered during golf course fairway renovation and construction. Mapping spatial variability of soil apparent electrical conductivity (ECa) has recently been suggested to have applications for precision turfgrass management (PTM) in native soil fairways, but sand-capped fairways have received les... C. Straw, B. Wyatt, A.P. Smith, K. Watkins, S. Hong, W. Floyd, D. Williams, C. Garza, T. Jansky |
189. Investigating the Behavior and Responses of Cage-free Laying Hens Using a Laser Disturbance SystemGrowing attention to animal welfare is accelerating the shift to cage-free housing, but floor eggs remain a persistent problem. Eggs laid on the ground are easily soiled, broken, and can transmit disease; they also raise labor and time costs because they must be collected quickly. Once floor laying becomes habitual, correction is difficult. We propose a laser-based disturbance system that uses non-invasive light cues to guide hens toward raised platforms and nest boxes. Deployed on an embedde... F. Chang |
190. Investigating the Potential of Visible and Near-infrared Spectroscopy (VNIR) for Detecting Phosphorus Status of Winter Wheat Leaves Grown in Long-term TrialThe determination of plant nutrient content is crucial for evaluating crop nutrient removal, enhancing nutrient use efficiency, and optimizing yields. Nutrient conventional monitoring involves colorimetric analyses in the laboratory; however, this approach is labor-intensive, costly, and time-consuming. The visible and near-infrared spectroscopy (VNIR) or hyperspectral non-imaging sensors have been an emerging technology that has been proved its potential for rapid detection of plant nutrient... Y. El-mejjaouy, B. Dumont, A. Oukarroum, B. Mercatoris , P. Vermeulen |
191. Investigation of Automated Analysis of Snowmelt from Time-series Sentinel 2 Imagery to Inform Spatial Patterns of Spring Soil Moisture in the American Mountain WestVariable rate irrigation of crops is a promising approach for saving water whilst maintaining crop yields in the semi-arid American Mountain West – much of which is currently experiencing a mega drought. The first step in determining irrigation zones involves characterizing the patterns of spatial variation in soil moisture and determining if these are relatively stable temporally in relation to topographic features and soil texture. Characterizing variable rate irrigation zones is usua... I. Turner, R. Kerry, R. Jensen, E. Woolley, N. Hansen, B. Hopkins |
192. Investigation of Seed Monitoring Potential Using Light Dependent Resistor (Ldr) for Cell Type Precision SeedersPrecision seeding is an important operation in modern agriculture, ensuring accurate seed placement at defined rates and intervals to optimize crop performance. Despite their critical importance, conventional seed metering devices often require frequent manual calibration, making them labor-intensive, inefficient, and impractical for both smallholder and large-scale farming operations. Existing seed monitoring technologies are often costly and lack real-time adaptability to varying field cond... S. Chung |
193. Is Row-unit Vibration Affected by Planter Speeds and Downforce?Row-unit vibration is an issue created mainly by planter`s opening disks and gauge-wheels contact with the ground. Variability on row-unit vibration could interfere on seed metering and delivery process, affecting crop emergence and final stand. With the amount of embedded technology present on planters, producers are being encouraged to increase planting speeds, which is also one of the main factors for row-unit vibration increasement. In this way, knowing the proper speeds, and using other ... L.P. Oliveira, B.V. Ortiz, G.T. Morata, T. Squires, J. Jones |
194. Knowledge-based Approach for Weed Detection Using RGB ImageryA workflow was developed to explore the potential use of Phase One RGB for weed mapping in a herbicide efficacy trial in wheat. Images with spatial resolution of 0.8 mm were collected in July 2020 over an area of nearly 2000 square meters (66 plots). The study site was on a research farm at the University of Saskatchewan, Canada. Wheat was seeded on June 29, 2020, at a rate of 75 seeds per square meter with a row spacing of 30.5 cm. The weed species seeded in the trial were kochia, wild oat, ... T. Ha, K. Aldridge, E. Johnson, S.J. Shirtliffe, S. Ryu |
195. Lameness Detection in Side-View Videos of Dairy Cows Based on Pose Estimation and Deep LearningLameness is a critical factor affecting milk production and remains a major concern in dairy farming. Conventional lameness detection relies on visual observation and veterinary judgment, which are subjective and labor-intensive. This study proposed a non-contact lameness detection system integrating pose estimation and machine learning. A YOLOv11-pose model was trained to detect cow keypoints, and features such as back curvature, head swing, and Back Posture Measurement (BPM) were extracted.... C. Chu |
196. Laser- Induced Enhancement of Seed Germination and Early Growth in LegumesLaser technologies are emerging as promising tools in precision agriculture for enhancing plant development and productivity. This study investigates the effects of low- power laser irradiation (532 nm, 1W) on the seed germination and early growth of mung beans (Vigna radiata). Seeds were exposed to laser light prior to planting, and their germination performance, leaf expansion, chlorophyll content, and shoot length were measured and compared to untreated control seeds. The laser-treated see... C. Ding |
197. Lauraceae Timber Identification Using Vision TransformerThe forest coverage in Taiwan exceeds 60%, yet over 99% of annual timber consumption relies on imports. This significant dependence, coupled with frequent incidents of wood misidentification and fraud, highlights the need for accurate and efficient wood species identification systems. Conventional approaches, such as microscopic analysis and sensory- based macroscopic inspection, are labor-intensive, subjective, and require domain expertise, making them unsuitable for large-scale or real-time... Y. Kuo |
198. Limitations of Yield Monitor Data to Support Field-scale ResearchPrecision agriculture adoption on farms continues to grow globally on farms. Today, yield monitors have become standard technologies on grain, cotton and sugarcane harvesters. In recent years, we have seen industry and even academics leveraging the adoption of precision agriculture technologies to conduct field-scale, on-farm research. Industry has been a primary driver of the increase in on-farm research globally through the development of software to support on-farm resear... J.P. Fulton, S.A. Shearer, A. Gauci, A. Lindsey, D. Barker, E. Hawkins |
199. Low Cost Smartphone Camera Accessory to Digitally Measure Leaf Color for Crop Nitrogen Status AssessmentCrop nitrogen (N) status is a desirable information for crop nutrition management. In addition to the traditional leaf sampling with subsequent laboratory analysis, the use of chlorophyll meters is a well-studied and accepted practice to indirectly measure crop N status. Nevertheless, chlorophyll meters are dedicated devices that still cost at least a few hundred dollars, thus being unsuitable to large scale use among low budget smallholders. Aiming to address this issue, a new low cost smart... G. Portz, S. Reusch, J. Jasper |
200. Low-code Development Environment and Middleware for Ubiquitous Environment Control SystemsThis work presents a low-code development environment that enables non-engineers to construct a customized software for UECS devices automating horticultural facilities as well as a middleware that provides a uniform application executing environment on different platforms for the UECS software. ... T. Nakanishi |
201. Machine Learning Prediction Models for Dual-Horizon Egg Production ForecastingEgg production forecasting presents significant challenges in agricultural supply chain management due to complex seasonal patterns, disease outbreaks, and market volatility. Although various forecasting models have been developed for agricultural production, limited research has systematically compared model performance across different temporal horizons or developed integrated frameworks optimized for diverse planning needs. This study develops a comparative dual-horizon machine learning fr... S. Chen |
202. Machine Learning Techniques for Early Identification of Nitrogen Variability in MaizeCharacterizing and managing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in-situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Indeed, practitioners of precision N management require determination of in-season plant N status in real-time at field scale to enable the most efficient N fertiliz... D. Mandal, R.D. Siqueira, L. Longchamps, R. Khosla |
203. Making Irrigator Pro an Adaptive Irrigation Decision Support SystemIrrigator Pro is a public domain irrigation scheduling model developed by the USDA-ARS National Peanut Research Laboratory. The latest version of the model uses either matric potential sensors to estimate the plant’s available soil water or manual data input. In this project, a new algorithm is developed, which will provide growers and consultants with much more flexibility in how they can feed data to the model. The new version will also run with Volumetric Water Content sensors, givin... I. Gallios, G. Vellidis, C. Butts |
204. Management Zone-specific N Mineralization Rate Estimation in Unamended SoilSince nitrogen (N) mineralization from soil organic matter is governed by basic soil properties (soil organic matter content, pH, soil texture, …) that are known to exhibit strong in-field spatial variability, N mineralization is also expected to exhibit significant spatial variability at field scale. An ideal and efficient N recommendation for precision fertilization should therefore account for potential soil mineralizable N considering this spatial variability. Therefore, this study... F.Y. Ruma, M.A. Munnaf, S. De neve, A.M. Mouazen |
205. Map Whiteboard As Collaboration Tool for Smart Farming Advisory ServicesPrecision agriculture, a branch of smart farming, holds great promise for modernization of European agriculture both in terms of environmental sustainability and economic outlook. The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rat... K. Charvat, R. Berzins, R. Bergheim, F. Zadrazil, J. Macura, D. Langovskis, H. Snevajs, H. Kubickova, S. Horakova, K. Charvat jr. |
206. Mapping Soil Health and Grain Quality Variations Across a Corn Field in TexasSoil health is a key property of soils influencing grain yield and quality. Within-field mapping of soil health index and grain quality can help farmers and managers to adjust site-specific farm management decisions for economic benefits. A study was conducted to map within-field soil health and grain protein and oil content variations using apparent electrical conductivity (ECa) and terrain attributes as their predictors. Two hundred and two topsoil samples were analyzed to determine soil he... K. Adhikari, D.R. Smith, C. Hajda, P.R. Owens |
207. MDPI - Agriculture and Agronomy Journals... N. Nišavić |
208. Measure, Model, Manage: the Unfinished Revolution in AgricultureOver the last 40 years, the paradigm of Measure, Model, Manage has promised an agricultural revolution through data-informed precision management. This shift remains largely incomplete, lagging concurrent innovations in genetics and pesticides. Significant barriers persist in achieving breakthrough innovations for crop data collection and the development of data analysis/decision-making systems. These hurdles include a decades-old "Sensor Crisis" (a lack of appropriate too... A. Werner, A. Holmes |
209. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile SensingSoils have a large carbon storage capacity and sequestering additional carbon in agricultural fields can reduce CO2 levels in the atmosphere, helping to mitigate climate change. Efforts are underway to incentivize agricultural producers to increase soil organic carbon (SOC) stocks in their fields using various conservation practices. These practices and the increased SOC provide important additional benefits including improved soil health, water quality and – in some cases –... E. Lund, T. Lund, C. Maxton |
210. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile SensingMeasuring soil carbon is currently a subject of significant interest due to soil’s ability to sequester carbon and reduce atmospheric CO2. The cost of conventional soil sampling and analysis along with the number of samples required make proximal sensing an appealing option. To properly evaluate the performance of proximal sensing of soil carbon, a detailed lab-analyzed carbon inventory is needed to serve as the ‘gold standard’ in evaluating sensor estimations. F... E. Lund |
211. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild BlueberryDeep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fie... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White |
212. Micro-climate Prediction System Using IoT Data and AutoMLMicroclimate variables like temperature, humidity are sensitive to land surface properties and land-atmosphere connections. They can vary over short distances and even between sections of the farm. Getting the accurate microclimate around the crop canopy allows farmers to effectively manage crop growth. However, most of the weather forecast services available to farmers globally, either by the meteorological department or universities or some weather app, provide weather forecasts for l... A. Sharma, R.S. Jalem, M. Dash |
213. Minnesota Corn Growers AssociationWith more than 6,500 members, the Minnesota Corn Growers Association is one of the largest grassroots farm organizations in the United States. Working in close partnership with the Minnesota Corn Research & Promotion Council, MCGA identifies and promotes opportunities for Minnesota’s 24,000 corn farmers while building connections with the non-farming public. We accomplish this by investing in third-party research that focuses on water quality and soil health, targeted consumer outre... M. Kazula |
214. Mobile-based Automated Phenotyping System for Accessible Tomato BreedingTomato breeding programs require extensive phenotypic data collection including fruit development stages and critical timing parameters, yet manual monitoring is labor- intensive and limits breeding program scalability, particularly in resource-limited environments. This study presents a cost-effective automated phenotyping system that requires only smartphone video recording combined with pre-assigned plot numbers, eliminating the need for expensive mobile platforms and making advanced breed... S. Chen |
215. Modeling and Characterization of Unimodal and Bimodal Diurnal Pollen Foraging Patterns in Honeybee ColoniesPollen foraging patterns in honeybee colonies provide essential information on their ecological adaptation strategies. This study proposes a statistical modeling framework to characterize diurnal pollen foraging patterns in honeybee colonies. To support this, data were collected from healthy honeybee colonies during controlled experimental period. The raw pollen harvest data were then segmented into daily time series and converted into hourly histograms to capture foraging rhythms more effect... T. Lin |
216. Modeling Spatial and Temporal Variability of Cotton Yield Using DSSAT for Decision Support in Precision AgricultureThe quantification of spatial and temporal variability of cotton yield provides critical information for optimizing resources, especially water. The Southern High Plains (SHP) of Texas is a major cotton (Gossypium hirsutum L.) production region with diminishing water supply. The objective of this study was to predict cotton yield variability using soil properties and topographic attributes. The DSSAT CROPGRO-Cotton model was used to simulate cotton growth, development and yield ... B.P. Ghimire, O. Adedeji, Z. Lin, W. Guo |
217. Modeling the Effects of Greenhouse Environmental Factors on Soft Rot Incidence in PhalaenopsisPhalaenopsis spp. is one of Taiwan’s most important ornamental crops for export. However, during greenhouse cultivation, Phalaenopsis is frequently threatened by bacterial soft rot (Erwinia spp.), particularly under high-temperature and high-humidity conditions that accelerate pathogen spread and cause severe losses in seedlings. This study was conducted in a Phalaenopsis greenhouse located in Houbi District, Tainan, Taiwan. The greenhouse contained 21 planting beds, which wer... C. Huang |
218. Modulated On-farm Response Surface Experiments with Image-based High Throughput Techniques for Evidence-based Precision AgronomyAgronomic research is vital to determining optimum inputs for crops to perform profitably at a local scale. However, the small-plot experiment validity is often uncertain due to on-farm variations. Furthermore, the likelihood of conducting a fully randomized trial at a local farm is low given various practical and technical challenges. We propose a new methodology with many inputs to allow for a response surface that fits the yield response to the input levels with higher accuracy to make on-... A.U. Attanayake, E.U. Johnson, H.U. Duddu, S.U. Shirtliffe |
219. Monitoring Chicken Houses with AI Surveillance SystemIn Taiwan, the need of chicken meat accounts for approximately 30% of total livestockvproduction. In order to maintaining animal welfare, floor-rearing chicken farming approaches are widely used in Taiwan. However, traditional poultry management is often labor-intensive which increases the risk of disease transmission. To improve monitoring efficiency, we proposed a smart rail surveillance system to automatically monitor chickens for real-time chicken health assessment. The system comprised a... Y. Kuo |
220. Multi-sensor Imagery Fusion for Pixel-by-pixel Water Stress MappingEvaluating water stress in agricultural fields is fundamental in irrigation decision-making, especially mapping the in-field water stress variability as it allows real-time detection of system failures or avoiding yield loss in cases of unplanned water stress. Water stress mapping by remote sensing imagery is commonly associated with the thermal or the short-wave-infra-red (SWIR) bands. However, integration of multi-sensors imagery such as radar imagery or sensors with only visible and near-i... O. Beeri, R. Pelta, Z. Sade, T. Shilo |
221. Multi-system Enhancement of Autonomous Field Vehicles for Crop Monitoring ApplicationsAutonomous field vehicles face operational challenges in agricultural environments, including terrain-induced instability, image quality degradation during motion, and limited operational endurance that compromise the reliability of data collection for precision agriculture applications. This study presents systematic improvements in three critical subsystems of autonomous vehicles for field-based crop monitoring: mobility optimization, visual stabilization, and power management. The study ad... S. Chen |
222. Multispectral Assessment of Chickpea in the Northern Great PlainsChickpea is an increasingly important crop in the Montana agricultural system. From 2017 to 2021 the U.S. has planted an average of about 492,000 acres per year with Montana chickpea production accounting for around 44% of the U.S. total (USDA/NASS QuickStats accessed on 2/11/2021). This has led to an increase in breeding efforts for elite varieties adapted to the unique conditions in the Northern Great Plains. Breeding of chickpea often relies on traditional phenotyping techniques that are l... J.M. Vetch |
223. Multivariate Linear Regression Modeling for Predicting Chicken Body Weight Using Age, Uniformity, and Growth RateAccurate estimation of chicken body weight is critical for optimizing feed management, harvesting schedules, and animal welfare in commercial poultry systems. This study proposes a robust predictive framework using multivariate linear regression to estimate the average weight of native broiler chickens based on three explanatory variables: age, uniformity, and daily growth rate. After rigorous data cleaning and outlier removal, the model was trained and validated on 43 field observations coll... H. Lin |
224. N-management Using Structural Data: UAV-derived Crop Height As an Estimator for Biomass, N Concentration, and N Uptake in Winter WheatIn the last 15 years, sensors mounted on Unmanned Aerial Vehicles (UAVs) have been intensively investigated for crop monitoring. Besides known remote sensing approaches based on multispectral and hyperspectral sensors, photogrammetric methods became very important. Structure for Motion (SfM) and Multiview Stereopsis (MVS) analysis approaches enable the quantitative determination of absolute crop height and crop growth. Since the first paper on UAV-derived crop height was published by Bendig e... G. Bareth, A. Jenal, H. Hüging |
225. Next in Precision Agriculture: Detecting and Correcting Pixels with Machinery Track Line Within FarmsWith more satellites orbiting the earth, monitoring of fields using satellite data has become easier and ubiquitous. Frequent observations of a field can provide vital cues about field health and management practices. However, farm analytical statistics derived from such datasets often need modification to create practical applications. This paper focuses on the detection and removal of field machinery track line pixels to reduce their effect on satellite-based agronomic recommendation and pr... G. Rathee, M. Sielenkemper |
226. Nighttime Piglet Detection Using Deep LearningIn 2023, Taiwan’s pig industry was valued at over NT$85.1 billion, representing nearly 40% of total livestock production. However, effective piglet management remains a challenge due to environmental variability, frequent aggressive behaviors, and labor shortages—especially during nighttime. Traditional monitoring methods rely on manual observation, which is time-consuming, subjective, and impractical for continuous surveillance. To address this, we propose an automated nighttime ... Y. Kuo |
227. Nitrogen Fertilization of Potato Using Management Zone in Prince Edward Island, CanadaPotato is sensible to nitrogen (N) and optimal N fertilization improve the tuber yield and its quality. Potato crop N response varies widely within fields. It is also well recognized that significant spatial and temporal variation in soil N availability occurs within crop fields. However, uniform application of N fertilizer is still the most common practice under potato production. Management zone (MZ) approach can help growers to achieve a part of this. The goal of the project is to compare ... A. Cambouris, M. Duchemin, N. Ziadi |
228. Nitrogen Placement Considerations for Maize Production in the Eastern US CornbeltProper fertilizer placement is essential to optimize crop performance and amount of applied nitrogen (N) along with crop yield potential. There exists several practices currently used in both research within farming operations on how and when to apply N to maize (Zea mays L). Split applications of N in Ohio is popular with farmers and provides an economic benefit but more recently some farmers have been using mid- and late-season N fertilizer applications for their maize production.&... J.P. Fulton, E. Hawkins, S. Shearer, A. Klopfenstein, J. Hartschuh, S. Custer |
229. Nitrogen Status Prediction on Pasture Fields Can Be Reached Using Visible Light UAV Data Combined with Sentinel-2 ImageryPasture fields under integrated crop-livestock system usually receive low or no nitrogen fertilization rates, since the expectation is that nitrogen demand will be provided by the soybean remaining straw cropped previously. However, keeping nitrogen at suitable levels in the entire field is the key to achieving sustainability in agricultural production systems. In this sense, remote sensing technologies play an essential role in nitrogen monitoring in pastures and crops. With the launch of th... F.R. Pereira, J.P. Lima, R.G. Freitas, A.A. Dos reis, L.R. Amaral, G.K. Figueiredo, R.A. Lamparelli, J.C. Pereira, P.S. Magalhães |
230. Non-destructive Tilapia Quality Determination Using Near-infrared SpectroscopyTilapia represents a significant economic asset in the aquaculture industry due to its high nutritional value and commercial importance. However, internal abnormalities are frequently detected during processing operations, particularly those caused by Streptococcosis, which is among the most prevalent diseases affecting tilapia quality. These quality defects often lead to commercial disputes between aquaculture farmers and fillet processors, highlighting the critical need for non-destructive ... S. Chen |
231. Null Dataset-Based Detection Enhances Robotic Vision in Greenhouse Cherry Tomato HarvestingCluttered cherry tomato greenhouse environments with visually similar distractors often trigger False Positives (FPs) in robotic vision, misguiding the robot’s motion and reducing harvesting success. We introduce a null-dataset strategy that integrates unannotated distractor images into YOLOv8l training, with their proportion tuned through loop refinement to suppress FPs while preserving precision. Optimal null proportions were identified as 12.3% for tomato detection and 8.3% for pedic... P. Yen |
232. On-the-go Gamma Spectrometry and Its Evaluation Via Support Vector Machines: Really a Valuable Tool for Site-independent Soil Texture Prediction?With progressive implementation of precision agriculture (PA) techniques in current agricultural/ viticultural practice, the need for high-resolution information on soil properties at low effort and cost is increasing. Moreover, climate change and extended drought periods do even increase this demand. Evaluating soil fertility and carbon storage potential of arable fields and vineyards, e.g. for future economic assessment of ecosystem services, requires spatially resolved soil data. Soil text... S. PÄtzold, T.W. Heggemann, R. Wehrle |
233. Optimization of Batch Processing of High-density Anisotropic Distributed Proximal Soil Sensing Data for Precision Agriculture PurposesThe amount of spatial data collected in agricultural fields has been increasing over the last decade. Advances in computer processing capacity have resulted in data analytics and artificial intelligence becoming hot topics in agriculture. Nevertheless, the proper processing of spatial data is often neglected, and the evaluation of methods that efficiently process agricultural spatial data remains limited. Yield monitor data is a good example of a well-established methodology for data processi... F. Hoffmann silva karp, V. Adamchuk, A. Melnitchouck, P. Dutilleul |
234. Optimizing Frost Prediction with a Multi-Window CNN–XGBoost Soft-Voting EnsembleRecent global climate change has increased the frequency of late-spring frost events, causing more severe and widespread damage to orchard growers. Frost formation occurs due to rapid temperature drops over short periods combined with overnight air stagnation; thus, effective prediction requires analyzing patterns across multiple time scales. We introduce a hybrid frost-forecasting framework that combines a multi-window 1-D convolutional neural network (CNN), utilizing 6-, 12-, and ... D. Kim |
235. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat ProductionField-specific fertilizer rate optimization is known to be beneficial for improving farming profit, and profits can be further improved by dividing the field into smaller plots and applying site-specific rates across the field. Finding optimal rates for these plots is often based on data gathered from said plots, which is used to determine a yield response curve, telling us how much fertilizer needs to be applied to maximize yield. In related work, we use a Convolutional Neural Network, known... A. Peerlinck, J. Sheppard, G.L. Morales luna, P. Hegedus, B. Maxwell |
236. Optimizing Power Delivery in Electric Farm Machinery Using a Hybrid Battery and Ultracapacitor SystemAgriculture plays a significant role in global greenhouse gas emissions, contributing notably to climate change. Integrating renewable energy into agricultural operations has become increasingly vital in addressing this challenge. This study investigates the potential of electrifying agricultural machinery using a hybrid energy storage system that combines batteries and ultracapacitors. While batteries offer high energy density, they face limitations such as slow charging and reduced lifespan... S. Wu-yang |
237. Organ Scale Nitrogen Map: a Novel Approach for Leaf Nitrogen Concentration EstimationCrop nitrogen trait estimations have been used for decades in the frame of precision agriculture and phenotyping researches. They are crucial information towards a sustainable agriculture and efficient use of resources. Remote sensing approaches are currently accurate tools for nitrogen trait estimations. They are usually quantified through a parametric regression between remote sensing data and the ground truth. For instance, chlorophyll or nitrogen concentration are accurately estimated usi... A. Carlier, S. dandrifosse, B. Dumont, B. Mercatoris |
238. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in ArgentinaThe lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomis... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito |
239. Performance Evaluation of Agricultural Spray Nozzle Under Different Pressure Conditions by Image AnalysisSpray nozzles are critical components in agricultural equipment used for pest control, pollination, and so on. The liquid ejected from the nozzle is broken down into droplets due to friction with the air and pressure changes. Consequently, the nozzle performance is often defined by alternative parameters to estimate the actual operating conditions. This study aims to determine the operating parameters of spray injection by photographing the movement of droplets ejected from a nozzle under dif... T. Okayasu |
240. Performance Study of Triboelectric Nanogenerator with Laser-induced Graphene ElectrodesAs wearable electronics increasingly demand a continuous power supply, conventional batteries—requiring frequent recharging or replacement—pose both user inconvenience and environmental risks. This study develops a wristwatch‐ shaped triboelectric nanogenerator that employs solid‐ state semiconductor laser‐ induced graphene electrodes patterned directly onto a polyimide (PI) film and utilizes an independent sliding interface to harvest 1 to 3 Hz low‐frequenc... C. Wu |
241. Pessl InstrumentsFor more than 37 years, Pessl Instruments has been offering tools for informed decision-making. A complete range of wireless, solar powered monitoring systems which support almost all communication standards roofed under the METOS® brand is available to our clients worldwide. The systems, along with online platform and mobile application Fieldclimate, are applicable in all climate zones and can be used in various industries and for various purposes – from ... D. Brazda |
242. Pest and Disease Image-text Identification System of Leafy Vegetables in Urban Community FarmingUrban community farming has been integrated into education for sustainable food and agriculture. However, the participants are primarily students and novice farmers with limited background knowledge. Managing pests and diseases becomes challenging for these growers as diverse vegetable crops attract various pest and disease species, requiring accurate identification and treatment expertise. There is a need to develop timely identification services and guidance on control measures. In the... S. Chen |
243. Phalaenopsis Seedling Assessment Using Leaf Contour Detection with YOLOIn this study, we propose a vision-based approach for automatically measuring the morphological traits of Phalaenopsis seedlings. By utilizing top-view and side-view images, our method automatically extracts leaf contours to replace traditional manual measurements. A YOLOv8n-seg model was employed to segment the seedlings, and further correction strategies were introduced to improve accuracy. Experimental results demonstrate the potential of our approach to support large-scale seedling classi... Y. Kuo |
244. Plantsaga: Integrating Segment Anything Model with Gaussian Splatting for Plant Organ-level 3d SegmentationOrgan-level 3D phenotyping is essential for crop breeding but remains limited by the high cost of manual annotations. To address this challenge, PlantSAGA (Plant Segment Anything Gaussian Splatting) is introduced as a reference-based framework that enables accurate organ segmentation with minimal annotation. Multi-view muskmelon plants were reconstructed using COLMAP for camera pose estimation and Gaussian Splatting for 3D modeling, while 1~10 reference masks guided organ-level discrimination... T. Lin |
245. Portable DNA Detection Tool for Halal Monitoring Using Spectral SensingPork and its derivatives are non-halal in Islam, raising concerns about cross- contamination in food. With the growing number of Muslim tourists and Taiwan’s efforts to expand its halal F&B exports, strict halal compliance and reliable detection methods are essential. Conventional techniques like PCR offer high accuracy but are limited by long processing times and the need for advanced laboratories. Recombinase Polymerase Amplification (RPA) presents a faster alternative, operating ... J. Chen |
246. Possibilities for Improved Decision Making and Operating Efficiency Derived from the Predictability of Autonomous Farming OperationsFor the last 6 years, small autonomous agricultural vehicles have been operating on Harper Adams University’s fields in Shropshire. Starting with a single tractor on a single rectangular hectare (2.5 acres) and moving on to three tractors on 5 irregularly shaped fields covering over 30 hectares (75 acres). Multiple crops have been grown; planting, tending, and harvesting with autonomous tractors and harvesters. The fields are worked using a Controlled Traffic Farming s... M. Gutteridge |
247. Potential of Plant Phenotyping for Data-driven Greenhouse HorticultureWe are trying to investigate the use of various features extracted from plant images for the purpose of environmental control in greenhouses according to the growth conditions of plants. A measurement robot was utilized in order to collect plant images. Plant growth features (apical buds, flowers, fruits, etc.) were extracted by using a deep learning-based detector. In addition, we also introduced a 3D reconstruction technology to obtain the plant shape features such as plant height, internod... T. Okayasu |
248. Potential of UAS Multispectral Imagery for Predicting Yield Determining Physiological Parameters of CottonThe use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationship... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash |
249. Power Consumption Signal Characterization of Bldc-based Agricultural Fans for Malfunction Detection for Smart GreenhousesEffective management of environmental parameters, notably temperature and humidity, is critical for ensuring optimal plant growth and productivity in smart greenhouses. Brushless (BLDC) fans are commonly utilized for controlling greenhouse ventilation and humidity levels. The primary aim of this study was to characterize the power consumption of BLDC agricultural fans to identify operational anomalies and facilitate predictive maintenance strategies. An experimental setup was devised, involvi... S. Chung |
250. Precise Strawberry Stem Localization Via Two-stage 3d Deep LearningHarvesting delicate fruits, such as strawberries, at their optimal ripening stage inherently presents significant challenges, given its labor-intensive, time-consuming nature and high susceptibility to mechanical damage. The solution lies in developing intelligent robotic harvesting systems that can accurately segment fruits, determine optimal picking locations, and perform delicate maneuvers without compromising fruit integrity. This study presents a novel 3D vision-based methodology for aut... C. Chang |
251. Precision Agriculture Education in Africa: Perceptions, Opportunities and Challenges, and the Way ForwardPrecision Agriculture is critical for accelerated transformation of the agrifood systems in Africa for shared prosperity and enhanced livelihoods. The paper presents an overview of the perceptions of faculty, undergraduate and postgraduate students from Ghanaian universities about PA education, and its opportunities and challenges. The study involves a case study of two public universities, the University of Cape Coast and the Technical University of Cape Coast, respectively a and a desk revi... K.A. Frimpong |
252. Precision Application of Seeding Rates for Weed and Nitrogen Management in Organic Grain SystemsIn a time of increasing ecological awareness, organic agriculture offers sustainable solutions to many of the polluting aspects of conventional agriculture. However, without synthetic inputs, organic agriculture faces unique challenges such as weed control and fertility management. Precision Agriculture (PA) has been used to successfully increase input use efficiency in conventional systems and now offers itself as a potential tool for organic farmers as well. PA enables on farm experimentati... S. Loewen, B.D. Maxwell |
253. Precision Nitrogen and Water Management for Optimized Sugar Beet Yield and Sugar ContentSugar beet (SB) production profitability is based on maximizing three parameters: beet yield, sucrose content, and sucrose recovery efficiency. Efficient nitrogen (N) and water management are key for successful SB production. Nitrogen deficits in the soil can reduce root and sugar yield. Overapplication of N can reduce sucrose content and increase nitrate impurities which lowers sucrose recovery. Application of N in excess of SB crop need leads to vigorous canopy growth, while compromising ro... O.S. Walsh, S. Shafian |
254. Precision Nutrient Management in the USA: Current Trends and Future OpportunitiesPrecision nutrient management (PNM) has become integral to modern U.S. agriculture, particularly in optimizing fertilizer use efficiency, reducing environmental impacts, and sustaining profitability. As detailed in recent analyses, the adoption of precision technologies for nutrient management in the U.S. is advanced, especially among large- scale operations in the Midwest Corn Belt. Key technologies facilitating PNM include variable rate technology (VRT), remote and proximal sensing, soil an... S. Phillips |
255. Predicting Below and Above Ground Peanut Biomass and Maturity Using Multi-target RegressionPeanut growth and maturity prediction can help farmers and breeding programs improving crop management. Remote sensing images collected by satellites and drones make possible and accurate crop monitoring. Today, empirical relations between crop biomass and spectral reflectance could be used for prediction of single variables such as aboveground crop biomass, pod weight (PW), or peanut maturity. Robust algorithms such as multioutput regression (MTR) implemented through multioutput random fores... M.F. Oliveira, F.M. Carneiro, M. Thurmond, M.D. Del val, L.P. Oliveira, B. Ortiz, A. Sanz-saez, D. Tedesco |
256. Predicting Corn Emergence Uniformity with On-the-go Furrow Sensing TechnologyIntegration of proximal soil sensors into commercial row-crop planter components have allowed for a dense quantification of within-field soil spatial variability. These technologies have potential to guide real-time management decisions, such as on-the-go variable seeding rate or depth. However, little is known about the performance of these systems. Therefore, research was conducted in central Missouri, USA to determine the relationship between planter sensor metrics, and corn (Zea mays ... L.S. Conway, C. Vong, N.R. Kitchen, K.A. Sudduth, S.H. Anderson |
257. Predicting Secondary Soil Fertility Attributes Using XRF Sensor with Reduced Scanning Time in Samples with Different Moisture ContentTo support future in situ/on-the-go applications using X-ray fluorescence (XRF) sensors for soil mapping, this study aimed at evaluating the XRF performance for predicting organic matter (OM), base saturation (V), and exchangeable (ex-) Mg, using a reduced analysis time (e.g., 4 s) in soil samples with different moisture contents. These attributes are considered secondary for XRF prediction because they do not present emission lines in the XRF spectrum. Ninety-nine soil samp... T.R. Tavares, J.P. Molin, T.R. Da silva , H.W. De carvalho |
258. Prediction of Lettuce Spad Value During Growth by a Multi-Spectral Image Sensor Using Machine Learning ModelIn this study, we aimed to improve previous LR (Linear regression) model for prediction of lettuce SPAD value, and used several machine learning (ML) models such as SVR (Support vector regression), KNN (K-nearest neighbors regression), KRR (Kernel ridge regression), DTR (Decision tree regression), RFR (Random forest regression), and ANN (Artificial neural network). K-means clustering algorithm was used to separate lettuce sample from background, and the reflectance from multi-spectral images ... H. Noh |
259. Preliminary Tests for Potato Yield Monitoring Using a Controlled Test BenchAccurate yield estimation is a critical aspect of precision agriculture, particularly for root crops such as potatoes, where direct measurement during harvest can be challenging and labor-intensive. Developing precise and automated methods to enhance the efficiency and accuracy of yield assessments is thus imperative. This study explores the potential of integrating vision-based imaging and non-contact sensing technologies to achieve accurate potato mass estimation under controlled laboratory... S. Chung |
260. Printed Nitrate Sensors for In-soil MeasurementsManaging nitrate is a central concert for precision agriculture, from delineating management zones, to optimizing nitrogen use efficiency through in-season applications, to minimizing leaching and greenhouse gas emissions. However, measurement methods for in-soil nitrate are limiting. State-of-the-art soil nitrate analysis requires taking soil or liquid samples to laboratories for chemical or spectrographic analysis. These methods are accurate, but costly, labor intensive, and cover limited g... C. Baumbauer, P. Goodrich, A. Arias |
261. Proximal Sensing of Penetration Resistance at a Permanent Grassland Site in Southern FinlandProximal soil sensing allows for assessing soil spatial heterogeneity at a high spatial resolution. These data can be used for decision support on soil and crop agronomic management. Recent sensor systems are capable of simultaneously mapping several variables, such as soil electrical conductivity (EC), spectral reflectance, temperature, and water content, in real-time. In autumn 2021, we used a commercial soil scanner (Veris iScan+) to derive information on soil spatial variability for a per... H.E. Ahrends, A. Lajunen |
262. Quantitative Assessment of Discharge Depth Effects on Lithium-Based Batteries: LTO, LFP, and NCMThis study explores the impact of depth of discharge (DoD) on the performance degradation of three lithium-based battery chemistries: lithium titanate (LTO), lithium iron phosphate (LFP), and nickel cobalt manganese oxide (NCM). The objective is to establish a standardized methodology for evaluating battery health under partial cycling and to quantify the degradation behavior across three DoD ranges: 0–33%, 34–66%, and 67–100%. LFP and NCM cells were cycled at 1C under room ... C. Huang |
263. Real-time Detection of Picking Region of Ridge Planted Strawberries Based on YOLOv5s with a Modified NeckRobotic strawberry harvesting requires machine vision system to have the ability to detect the presence, maturity, and location of strawberries. Strawberries, however, can easily be bruised, injured, and even damaged during robotic harvest if not picked properly because of their soft surfaces. Therefore, it is important to cut or pick the strawberry stems instead of picking the fruit directly. Additionally, real-time detection is critical for robotic strawberry harvesting to adapt to the chan... Z. He, K. Manoj, Q. Zhang, S. Kshetri |
264. Realising the Potential of Agricultural Robotics and AI: The Ethical ChallengesRecent advances in AI and robotics may dramatically transform agriculture by greatly expanding the number of contexts in which the techniques of precision agriculture may be applied. Inevitably, this next agricultural revolution will generate profound ethical issues: opportunities as well as risks. Clever applications of AI and robotics may allow agriculture to be more sustainable by facilitating more precise applications of water, fertilisers, and herbicides. Robots may take some of the drud... R. Sparrow |
265. Reducing Ground Losses Using a Leaf Segmentation-based Autonomous Sprayer for Papaya GreenhousesPapaya plants have irregular canopy structures, making traditional spraying methods highly labour-intensive and prone to chemical waste due to non-selective application. In precision agriculture, delivering pesticides accurately to target areas is crucial for reducing labour requirements, costs, and environmental impact. Therefore, the integration of smart agricultural machinery and machine vision is necessary to optimise pesticide application. In this study, a low-cost autonomous spraying sy... W. Lin |
266. Regression Model for Estimating Branch Number of Soybean Using Uav-based Multispectral ImagesSoybean (Glycine max (L.) Merr.) is a protein-rich crop, and the number of branches is a significant trait associated with yield. This study aims to estimate the branch number of soybeans using vegetation indices (VIs) extracted from multispectral images mounted on a UAV. The study was conducted on the soybean cultivar Seonpung, sown on June 20, 2022, and June 24, 2023. Vegetation growth was investigated on 20 control and 30 treatment samples on August 20 and September 20, 2022, August 21 and... C. Ryu |
267. Revolutionizing Poultry Health: AI-Powered Real-Time Disease Detection Using YOLO v7 and IQR for Enhanced Farm ProductivityPrompt and accurate detection of poultry diseases is crucial to prevent outbreaks and reduce economic losses. Conventional monitoring systems based on manual inspections are inefficient and prone to error, delaying timely interventions. This study proposes an AI-driven early warning system that integrates YOLO v7 for real-time image detection with Hampel Filters for anomaly recognition. The model specifically targets two critical health indicators: rooster combs and eyes. Over a period of 53 ... A. Santosa |
268. Rgb-based Soil Water Content Prediction Enhanced by Hyperspectral CalibrationWhile hyperspectral imaging (HSI) cameras demonstrate high accuracy for detecting soil water content (SWC)-related spectral variations, their field deployment remains constrained by prohibitive costs and operational complexity. This study investigates utilizing low-cost RGB cameras through HSI-guided calibration for SWC estimation. 210 paired HSI-RGB measurements were acquired across five soil texture classes (0-40% fine particles), fourteen moisture levels (0-39% SWC), and three illumination... J. Park |
269. Robot Safety Issues in Field Crops - EU Regulatory Issues and Technical AspectsThe use of robots in Precision Agriculture is becoming of great interest, but they introduce a new kind of risk in the field due to their self-acting and self-driving capability. Safety issues appear with respect to people working in the same field in human-robot collaboration (HRC) framework or to the accidental presence of humans or animals. A robot out of control may also invade other areas causing unpredictable harm and damage. Currently, the safety of highly automated agricultu... M. Canavari, P. Lattanzi, G. Vitali, L. Emmi |
270. Robotic Arm Tomato Harvesting System and Next Best View Algorithm DevelopmentReplacing human labor with robots is a trend for future agriculture due to its efficiency and consistency. However, in automatic fruit harvesting tasks, leaf occlusion and the dynamic orientation of fruit make it difficult for robots to directly observe the picking point. To address this problem, this research focuses on tomato harvesting, and proposes a next-best-view (NBV) algorithm based on two main structures: “tomato pose prediction” and a “target-hit-gain function&rdqu... P. Yen |
271. Scaling Up Window-based Regression for Crop-row DetectionCrop-row detection is a central element of weed detection and agricultural image processing tasks. With the increased availability of high-resolution imagery, a precise locating of crop rows is becoming practical in the sense that the necessary data are commonly available. However, conventional image processing techniques often fail to scale up to the data volumes and processing time expectations. We present an approach that computes regression lines ... A.M. Denton, G.E. Hokanson, P. Flores |
272. Seed Localization System Suite with CNNs for Seed Spacing Estimation, Population Estimation and DoublesProper seed placement during planting is critical to achieve uniform emergence which optimizes the crop for maximum yield potential. Currently, the ideal way to determine planter performance is to manually measure plant spacing and seeding depth. However, this process is both cost- and labor-intensive and prone to human errors. Therefore, this study aimed to develop seed localization system (SLS) system to measure seed spacing and seeding depth and providing the geo-location of each planted s... A. Sharda, R. Harsha chepally |
273. Should We Increase or Decrease the Fertilization in the Zones with the Highest Crop Productivity Potential?Introduction. In traditional farming, fertilizers are applied homogeneously on the agricultural fields taking into account the average crop recommendation. As most fields are not homogeneous, this results in overfertilization of certain zones and underfertilization of other zones. The excess of nitrate leaches to the surface and groundwaters which causes problems with the water quality. Precision fertilizer management has been proposed to reduce these negative e... A. Tsibart, A. Postelmans, J. Dillen, A. Elsen, G. Van de ven, W. Saeys |
274. Signal Characterization for Actuator Operation Status Monitoring in Smart Vertical FarmsVertical farming presents a sustainable solution for high-yield crop production in space- constrained environments by enabling precise control over environmental parameters. However, effective implementation depends not only on environmental monitoring but also on the reliable operation of actuators that regulate system condition. The objective of this study was to characterize power consumption signals from actuators within smart vertical farms to facilitate precise monitoring, assessment of... S. Chung |
275. Signal Characterization of Environmental Sensors for Abnormality Detection in Hot Temperature GreenhousesMaintaining optimal microclimatic conditions is critical for crop productivity in greenhouse cultivation. High-temperature environments can induce subtle but critical deviations in environmental parameters, often resulting in reduced crop growth, quality, and yield. This study aimed to characterize the raw signal behavior of environmental sensors to enable early detection of abnormal conditions in hot-temperature greenhouses. An internet of things (IoT)-based sensor network comprising tempera... S. Chung |
276. Signal Characterization of Ict Components for Malfunction Detection for Open-field Irrigation SystemsAgricultural practices in open fields increasingly rely on automated irrigation technologies and ICT components, whose operational status impacts their reliability and efficiency. This study aimed to develop a malfunction detection pattern for sensors and actuators through signal characterization in an open-field irrigation setup. The experiment included environmental sensors and actuators, interfaced with a programmed microcontroller, operating in cycles (On/Off) or alternatively. Signals we... S. Chung |
277. Signal Characterization of Sensors for Operational Status Monitoring in Smart Vertical FarmsVertical farming represents an advanced agricultural practice capable of efficiently producing high-quality crops through precise environmental management, optimal spatial utilization, and consistent production outcomes. Ensuring reliable and accurate performance of environmental sensors is essential for sustaining ideal growth conditions within these advanced agricultural systems. This study aimed to characterize signals from environmental sensors to enhance real-time operational status moni... S. Chung |
278. Simulation and Control of Brushless DC Motors Based on Fuzzy PID for Unmanned Vehicles in Poultry HousesThe poultry industry holds a significant position in the development of Taiwan's agricultural economy, with commercial broiler and layer chicken farming constituting its primary sectors. The rampant spread of avian influenza has resulted in the mass mortality of broiler and layer chickens, leading to substantial economic losses.To mitigate the risk of avian influenza infection and reduce labor costs, this study investigates the application of a FUZZY PID controller for brushless DC motors... Z. You |
279. Six-Axis Robotic Arm and Object 3D Detection Technique for Supporting Mobility-Limited People on Grasping ObjectsThis paper presents the integration of a six-axis semi-industrial robotic arm with a real-time 3D object detection system to enable intuitive, contactless human-robot interaction, with a particular focus on healthcare applications. The robotic arm, powered by high-precision stepper motors, delivers enhanced accuracy and reliability compared to traditional servo-based systems, making it ideal for tasks that demand precision and consistency. The system is designed for extensibility, suppor... J. Chou |
280. Smart Food Oases: Development of a Distributed Point-to-point Urban Food Ecosystem in Food Desert AreasUrban agriculture has been getting much attention in the past decade as a solution to overcome food insecurity and accessibility of food for urban residents and to have better green environments in cities. Urban agriculture is expected to provide better nutrients to residents, reduce transportation and environmental costs, and help urban dwellers access food efficiently. The present study is to build a collaborative ecosystem among urban growers/producers and create bridges from these farmers... J. Lee, S. Song, S. Oh, K. Krishnaswamy, C. Sun, Y. Adu-gyamfi |
281. SmartAgriHubs FIE20 - Groundwater and Meteo Sensors and Earth Observation for Precision AgricultureThe solution developed under the SmartAgriHubs project in the scope of the Flagship Innovation Experiment FIE20 Groundwater and meteo sensors is an expert system to support farmers in decision-making process and planning process of field interventions. This FIE20 solution integrates various data sources and different analytical processes in a complete system and provides users an easy-to-use web map application as a common user interface. The FIE20 system integrates components developed durin... K. Charvat, M. Kepka, R. Berzins, F. Zadrazil, D. Langovskis, M. Musil |
282. Smartflow: Ai Optimization of Desalination for Sustainable Agricultural Water ManagementLimited access to reliable freshwater sources is a persistent barrier to agricultural productivity, particularly in coastal and arid regions where rivers, lakes, and groundwater reserves are rapidly declining. Farmers in these areas often struggle to meet irrigation demands, resulting in reduced yields and heightened vulnerability to climate variability. Although seawater desalination provides a potential alternative, conventional reverse osmosis (RO) systems are typically too energy-intensiv... M. Jamaludin |
283. Smartphone Application for Real-time Environment Monitoring of Smart GreenhousesSmart greenhouse technologies significantly enhance agricultural productivity, sustainability, and resource efficiency, yet existing solutions often face limitations regarding affordability, real-time responsiveness, and scalability, especially for small- and medium-sized farms. This research introduces a cost-effective, scalable smartphone- based application designed for real-time monitoring and precise control of essential greenhouse environmental parameters, including temperature, relative... S. Chung |
284. Snap-shot Hyperspectral Camera for Potassium Prediction of Peach Trees Using Multivariate AnalysisHyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or pest disease detection. As sensing technology advances, measuring nutrient levels and disease detection also progresses. This study aimed to predict the levels of potassium (K) content in peach leaves with the new snapshot hyperspectral camera. The study was conducted at the Clemson University Musser Fruit Research Farm (Seneca, SC, USA, 34.... J.J. Maja, M. Abenina, M. Cutulle, J. Melgar, H. Liu |
285. Soil and Crop Factors to Site-specific Nitrogen Management on Sugarcane FieldsNitrogen (N) is one of the most widely used fertilizers in crops and the most harmful to the environment. The increase fertilizers consumption, mainly N sources (one of the most widely fertilizer used in sugarcane fields), is one of the main factors underlying the sustainability of the entire production process. Currently, N recommendations in sugarcane are based only on the expected yield. However, there is little agronomic support for nitrogen (N) recommendations based on expected yield, de... G.M. Sanches, R. Otto, F.R. Pereira |
286. Soil Moisture Variability on Golf Course Fairways Across the United States: an Opportunity for Water Conservation with Precision IrrigationFairways account for an average of 11.3 irrigated hectares on each of the 15,000+ golf courses in the US. Annual median water use per hectare on fairways is between ~2,800,000 and 14,000,000 liters, depending on the region. Conventional fairway irrigation relies on visual observation of the turfgrass, followed by secondary considerations of short-term weather forecasts, which oftentimes lead to “blanket” applications to the entire area. The concept of precision irrigation is a str... C. Straw, C. Bolton, J. Young, R. Hejl, J. Friell, E. Watkins |
287. Soil Variability Mapping with Airborne Gamma-ray Spectrometry and MagneticsThe knowledge of spatial distribution of agricultural soils physical and chemical properties is critical for profitable and sustainable crop and food production. The collection of soil data presents however obvious problems arising from sampling a dense, opaque and very heterogeneous medium. Conventional methods consisting of ground-based grid survey are laborious, expensive and lack appropriate spatial resolution to allow best farm management decision. Over the past 50 years, airborne geophy... L. Ameglio, E. Stettler, D. Eberle |
288. Soil, Landscape, and Weather Affect Spatial Distributions of Corn Population and YieldAs more planters are equipped with the technology to vary seeding rate, evaluation of the within-field relationships between plant stand density (or population) and yield is needed. One aspect of this evaluation is determining how stand loss and yield are related to soil and landscape factors, and how these relationships vary with different weather conditions. Therefore, this research examined nine site-years of mapped corn yield, harvest population, and soil and landscape data obtained for a... K.A. Sudduth, N.R. Kitchen, L.S. Conway |
289. SoilView, LLCSoilView is an independent provider specializing in precision sampling and field services for agriculture retail, research groups, universities, and the evolving carbon market. Our areas of expertise include sampling for soil nutrients, carbon sampling, soil health and biology, and custom sampling processes for field research. We aim to remove the burden of sample collection for our customers by expertly managing all steps from field collection to final data delivery. Our... R. Shorkey |
290. Soybean Variable Rate Planting Simulator Using Economic ScenariosSoybean seed costs have increased considerably over the past 15 years, causing a growing interest in variable rate planting (VRP) to optimize seeding rates within soybean fields. We developed a publicly available online Soybean Variable Rate Planting Simulator (http://analytics.iasoybeans.com/cool-apps/SoybeanVRPsimulator/) tool to help farmers, agronomists, and other agriculturalists to understand the essential prerequisite agronomic or economic conditions necessary for profitable VRP implem... B. Mcarthor , A. Prestholt, P. Kyveryga |
291. Spatial Analysis of Soil Moisture and Turfgrass Health to Determine Zones for Spatially Variable Irrigation ManagementThe Western United States is currently experiencing a “Mega Drought”. This makes efficient water use more important than ever. Turfgrass is a major vegetation type in urban areas and performs many ecosystem services such as cooling through evapotranspiration, fixing carbon from the atmosphere and reducing wild-fire risk. There are now more acres of irrigated turfgrass (>40 million) in the USA than irrigated corn, wheat and fruit trees combined (Milesi et al., 2005). It has been... R. Kerry, S. Shumate, B. Ingram, K. Hammond, D. Gunther, R. Jensen, S. Schill, N. Hansen, B. Hopkins |
292. Spatial and Temporal Factors Impacting Incremental Corn Nitrogen Fertilier Use EfficiencyCurrent tools for making crop N fertilizer recommendations are primarily based on plot and field studies that relate the recommendation to the economic optional N rate (EONR). Some tools rely entirely on localized EONR (e.g., MRTN). In recent years, tools have been developed or adapted to account for within-field variation in crop N need or variable within season factors. Separately, attention continues to elevate for how N fertilizer recommendations might account for environmenta... N.R. Kitchen, C.J. Ransom, J.S. Schepters, J.L. Hatfield, R. Massey |
293. Spatially Explicit Prediction of Soil Nutrients and Characteristics in Corn Fields Using Soil Electrical Conductivity Data and Terrain AttributesSite specific nutrient management (SSNM) in corn production environments can increase nutrient use efficiency and reduce gaseous and leaching losses. To implement SSNM plans, farmers need methods to monitor and map the spatial and temporal trends of soil nutrients. High resolution electrical conductivity (EC) mapping is becoming more available and affordable. The hypothesis for this study is that EC of the soil, in conjunction with detailed terrain attributes, can be used to map soil nutrient... S. Sela, N. Graff, K. Mizuta, Y. Miao |
294. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep LearningUnmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniqu... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun |
295. SpringerSpringer is a leading global scientific, technical and medical publisher, providing researchers in academia, scientific institutions and corporate R&D departments with quality content via innovative information products and services. Springer is part of Springer Nature, one of the world’s leading global research, educational and professional publishers. ... |
296. Stem Characteristics and Local Environmental Variables for Assessment of Alfalfa Winter SurvivalAlfalfa (Medicago sativa L.) is considered the queen of forage due to its high yield, nutritional qualities, and capacity to sequester carbon. However, there are issues with its relatively low persistency and winter survival as compared to grass. Winter survival in alfalfa is affected by diverse factors, including the environment (e.g., snow cover, hardiness period, etc.) and management (e.g., cutting timing, manure application, etc.). Alfalfa's poor winter survival reduces the number of ... M. Saifuzzaman, V. Adamchuk, M. Leduc |
297. Strawberry Pest Detection Using Deep Learning and Automatic Imaging SystemStrawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality. However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cam... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez |
298. Study of Non-contact Respiration and Temperature-humidity Index for Dairy Cow Heat Stress MonitoringThis thesis develops a non-contact system to automatically monitor the respiratory rate of dairy cows, aiming to improve real-time health assessment and management in livestock farming. Respiratory rate is a key indicator of cow health, helping detect heat stress, respiratory illness, and other conditions early. The system uses visible light and thermal cameras to capture synchronized videos, while image recognition algorithms detect and track the nasal region to perform respiratory measureme... W. Chu |
299. Study on Contect Sensor-based Ridge Tracking Technology for Precision Garlic SeedingRidges are an important part of field operations in agriculture. From soil tillage and sowing to harvesting, ridges serve as the foundation throughout the entire crop production cycle. However, in practical application, ridges are often irregular and poorly maintained. Irregular ridge can disrupt consistent seeding which can result in uneven crop growth and a decline in overall productivity. In the case of garlic, seeding uniformity is directly related to yield. Therefore, addressing the unev... H. Kim |
300. Suitability of ML Algorithms to Predict Wild Blueberry Harvesting LossesThe production of wild blueberries (Vaccinium angustifolium.) is contributing 112.2 million dollars to the Canada’s revenue which can be further increased through controlling harvest losses. A precise prediction of blueberry harvesting losses is necessary to mitigate such losses. In this study, the performance of three machine learning (ML) models was evaluated to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada we... H. Khan, T. Esau, A. Farooque, F. Abbas |
301. Sun Effect on the Estimation of Wheat Ear Density by Deep LearningEar density is one of the yield components of wheat and therefore a variable of high agronomic interest. Its traditional measurement necessitates laborious human observations in the field or destructive sampling. In the recent years, deep learning based on RGB images has been identified as a low-cost, robust and high-throughput alternative to measure this variable. However, most of the studies were limited to the computer challenge of counting the ears in the images, without aiming to convert... S. Dandrifosse, E. Ennadifi, A. Carlier, B. Gosselin, B. Dumont, B. Mercatoris |
302. Supervised Feature Selection and Clustering for Equine Activity RecognitionIn this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a m... T. De waele, D. Peralta, A. Shahid, E. De poorter |
303. Survey of Pesticide Application Practices and Technologies in Georgia Agricultural CropsGeorgia is a leading producer of numerous crops including cotton, peanut, blueberries, pecans, bell peppers, cabbage, watermelons, and peaches in the United States. Pesticide applications are critical for the successful production of these crops. Pesticide regulations and application technologies are changing rapidly due to growing concerns around off-target movement and increased focus on improving the efficiency and efficacy of pesticide applications. In order to provide suitable ... S.S. Virk, E.P. Prostko |
304. Survey Shows Specialty and Commodity Crop Retailers Use Precision Agriculture DifferentlyThe 2021 CropLife-Purdue Survey of precision agricultural practices by US agricultural input dealers serving the American grain and oilseed sector shows that most of them use GPS guidance and related technologies like sprayer boom control, most provide variable rate fertilizer services, and the majority say that fertilizer decisions are influenced by grower data. In contrast, dealers serving horticultural and specialty crop farms indicate comparatively modest adoption of many precision agricu... B.J. Erickson, J. Lowenberg-deboer |
305. Sweet Potato Skin Color Analysis Algorithm Based on Image Preprocessing and Color ModelsSweet potato breeding typically requires eight to ten years, during which phenotypic trait measurement and cultivar selection demand substantial labor and time. The Chiayi Agricultural Experiment Station in Taiwan, which hosts the nation’s largest sweet potato germplasm collection, relies heavily on manual phenotypic trait evaluations throughout the breeding process. In particular, the assessment of skin color is highly susceptible to variations in lighting conditions and subjective jud... T. Lin |
306. Synchronized Windrow Intelligent Perception System (SWIPE)The practice of bale production, in forage agriculture, involves various machines that include tractors, tedders, rakers, and balers. As part of the baling process, silage material is placed in windrows, linearly raked mounds, to drive over with a baler for easy collection into bales. Traditionally, a baler is an implement that is attached on the back of a tractor to generate bales of a specific shape. Forage agricultural equipment manufacturers have recently released an operator driven, self... E.M. Dupont, P.R. Kolar |
307. Synthetic Data-driven Validation of Multi-stage Fruit Detection Systems in Controlled Virtual EnvironmentsAccurate fruit counting across development stage is critical for tomato breeding decisions. Yet, the ground truth validation in real field remains challenging where partially occluded fruits cannot be reliably counted manually due to complex environmental factors. To address this need, this study presents a photorealistic simulation approach that complements real field data collection. A virtual environment enables controlled evaluation across three distinct fruit growth stages: green stage f... S. Chen |
308. Teaching Mathematics Towards Precision Agriculture Through Data Analysis and ModelsPrecision agriculture is used in a wide variety of field operations and agricultural practices that affect our daily lives. Many fields of agriculture are increasingly adopting equipment automation, robotics, and machine learning techniques. These all lead to recognize that data collection and exploitation is a valuable tool assisting in real-time farming and livestock decisions. Thus, the immediate need to empower students in Agriculture Sciences with mathematical tools using data analysis i... R. Sviercoski |
309. Temperature Effect on Wild Blueberry Fruit Quality During Mechanical HarvestMechanical harvesters, utilizing a range of technologies, have been developed for timely operations and remain the most cost-effective means of picking the wild blueberry crop. Approximately 95% of wild blueberries in Atlantic Canada are immediately frozen and processed, while only a small percentage is sold in the fresh market. However, the producers can benefit by increasing the value of their harvested crop through fresh market sales. The objective of this study was to determine the optimu... T.J. Esau, A.A. Farooque, F. Abbas |
310. The Development of a Real-time Monitoring System Using IoT Sensor TechnologyThis study developed an IoT-based monitoring system for cold storage of agricultural products. Using temperature-humidity, CO₂, and ethylene sensors with Raspberry Pi, real-time data were collected and analyzed. Field tests showed stable monitoring of temperature (3~8 °C), humidity (77~92%), and CO₂ (450~1400 ppm), while no ethylene was detected. The system demonstrated reliable performance and potential to improve quality control and efficiency in post-harvest storage. ... H. Joo kim |
311. The Effect of Slope Gradient on the Modelling of Soil Carbon Dioxide Emissions in Different Tillage Systems at a Farm Using Precision Tillage Technology in HungaryUnderstanding the role of natural drivers in greenhouse gas (GHG) emitted by agricultural soils is crucial because it contributes to selecting and adapting acceptable eco-friendly farming practices. Hence, Syngenta Ltd. collaborating with researchers, aimed to investigate the effect of two tillage treatments, conventional-tillage (CT) and minimum-tillage (MT) on soil carbon dioxide (CO2) emissions. The research field is in Hungary. Soil columns were derived from different tillage s... I.M. Kulmany, S. Benke, L. Bede, R. Pecze, V. Vona |
312. The Effects of Thermal Aging and Ultraviolet Radiation Aging on the Performance of Greenhouse Plastic Films with Different ThicknessesDue to Taiwan’s hot and humid climate, it is necessary to consider the haze, tensile strength, and aging resistance of greenhouse plastic films of different thicknesses to evaluate whether replacement is required. This study focuses on commonly used commercial plastic films in Taiwan’s agricultural facilities, mainly composed of linear low-density polyethylene (LLDPE), with thicknesses of 0.15 mm, 0.18 mm, and 0.20 mm. These greenhouse films were subjected to artificial accelerate... L. Chen-chang |
313. The ISO Strategic Advisory Group for Smart Farming: a Multi-pronged Opportunity for Greater Global InteroperabilityAgriculture is becoming increasingly complex and producers must secure their profitability, sustainability, and freedom to operate under a progressively more challenging set of constraints such as climate change, regulatory pressure, changes in consumer preferences, increasing cost of inputs, and commodity price volatility. We have not, however, yet reached the level of data interoperability required for a truly "smart" farming that can tackle the aforementioned probl... R. Ferreyra, J. Lehmann |
314. The Use of Spatial and Temporal Measures to Enhance the Sensitivity of Satellite-based Spectral Vegetation Indices to (Water) Stress in Maize FieldsClimate change and water scarcity are reducing the available irrigation water for agriculture thus turning it into a limited resource. Today calculating and estimating crop water requirements are achieved through the ETc FAO-56 model where the effect of climate on crop water requirement is determined through the water evaporation from the soil and plant (ETref), and a calendar crop coefficient (Kc). Models t... Y. Goldwasser, V. Alchanati, E. Goldshtein, Y. Cohen, A. Gips, I. Nadav |
315. Theoretical Analysis of Deflection in Deformed Silicone Components for Dried Longan PeelingIn traditional manual processing of dried longan, the fruit is typically peeled by cutting from the stem end with a knife and tearing along the seed axis to separate the flesh. However, to enhance operational efficiency and realize production automation, the development of dried longan processing machinery with automatic peeling capabilities has become an inevitable trend in the industry. The most critical component of such machines is the peeling module, whose geometry and dimensions directl... C. Cheng |
316. Theoretical Analysis of Deflection in Deformed Silicone Components for Dried Longan PeelingIn traditional manual processing of dried longan, the fruit is typically peeled by cutting from the stem end with a knife and tearing along the seed axis to separate the flesh. However, to enhance operational efficiency and realize production automation, the development of dried longan processing machinery with automatic peeling capabilities has become an inevitable trend in the industry. The most critical component of such machines is the peeling module, whose geometry and dimensions directl... C. Cheng |
317. Theoretical Power Analysis of a Driving Unit for a Sweet Potato Harvester Under DevelopmentMechanized harvesting has become increasingly essential in modern agriculture to enhance productivity and reduce labor dependency, particularly for root crops like sweet potatoes, which traditionally involve intensive manual labor. This study presented a theoretical analysis of a driving mechanism for a sweet potato harvester under development. A theoretical analysis was conducted to evaluate the power requirements, torque distribution, and transmission efficiency of the mechanism. This analy... S. Chung |
318. Thermoelectric Infrared Sensor Integrated with SHA AbsorberThis paper details the design of a high-performance thermoelectric infrared (IR) sensor using the UMC 0.18 μm CMOS-MEMS process, targeting the 8–14 μm wavelength for applications like IoT. To enhance performance, the sensor integrates two key innovations: a Sub-Wavelength Hole Array (SHA) absorber and a novel double-layer thermopile structure with 64 pairs of thermocouples. Finite-Difference Time-Domain (FDTD) simulations show the SHA structure achieves an average IR absorptivity ... Z. Dai |
319. Toward Smart Soybean Variety Selection Using UAV-based Imagery and Machine LearningThe efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield and resilience to stress, achieved in one year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials due to their large population, complex genetic behavior, and high genotype-en... J. Zhou, J. Zhou |
320. Towards in Situ Monitoring of Root Growth Traits: Combining Spectral Imaging with Transparent Bed HydroponicsWe developed a novel method that enables non-laboratory monitoring of the growth characteristics of crop root systems by combining spectral imaging with a transparent bed hydroponics. Root systems of spinach grown were observed through the transparent bottom plate using a hyperspectral camera daily. An optimal index for the classification of root ages (days after emergence) was determined as the ratio of reflectance at 498 and 601 nm. Additionally, the distribution of root age was visualized ... D. Yasutake |
321. UAV-based Hyperspectral Monitoring of Peach Trees As Affected by Silicon Applications and Water Stress StatusPrevious research has shown that the application of reduced doses of Silicon (Si) improves crop tolerance to water stress, which is common in commercial young peach trees because irrigation is not usually applied during their first two years. In this study, aerial images were used to monitor the impact of different Si and water treatments on the hyperspectral response of peach trees. An experiment with 60 young (under 1 year old) peach trees located at the Musser Fruit Research Center (Seneca... J. Peña, J. Melgar, A. De castro, J. Maja, K. Nascimento-silva |
322. Unlocking Canopy Dynamics: Uav-lidar-based Biomass Estimation in Ocimum BasilicumUAV-LiDAR offers a high-throughput route to phenotyping and biomass estimation in basil (Ocimum basilicum L.). Over three crops seasons (2021–2023), we evaluated three commercial varieties across 96 plots under different irrigation regimes and sowing densities. Multi-temporal LiDAR acquisitions quantified canopy height, LAI and volume and were validated against ground truth. Canopy volume strongly predicted fresh biomass (R² = 0.93; mean error < 8%). Across years, fresh bio... P. Toscano |
323. Unsupervised Anomaly Detection of Tipburn in Leafy Vegetables Using Denoising AutoencoderTipburn, a common physiological disorder in leafy vegetables, presents as marginal necrosis but its fuzzy boundaries make annotation costly and inconsistent. We present a label-free pipeline that combines CIE Lab–based preprocessing with a chroma-only denoising autoencoder (DAE) trained solely on healthy samples for real-time, pixel-level anomaly mapping. Lettuce images were acquired under controlled lighting, segmented in CIE Lab space, and reduced to the a channel and a/b chromatic ra... M. Yang |
324. Unsupervised Hyperspectral Image Segmentation Using Deep Global ClusteringHyperspectral imaging (HSI) combines rich spectral and spatial information, supporting field monitoring and crop assessment in precision agriculture. HSI scenes from one dataset usually share the same background and foreground classes, yet spectra from one region differ from those in another. Pixels that describe the same object therefore cluster together in spectral space; mapping these clusters back onto the image yields pseudo-segmentations that can stand in for class labels. However, proc... S. Chen |
325. Use of MLP Neural Networks for Sucrose Yield Prediction in SugarbeetINTRODUCTION Sugar beet is one of the more technified agro industries in Spain. In the last years, it has leaded as well the digital transformation with the objective of maintaining sugar beet competitivity both national and internationally. Among other lines, very high potential has been identified in determining the sucrose content using a combination of Artificial Intelligence and Remote Sensing. This work presents the conclusions of an extensive data acquisition task, creation o... M. Cabrera dengra, C. Ferraz pueyo, V. Pajuelo madrigal, L. Moreno heras, G. Inunciaga leston, R. Fortes |
326. Use of Precision Technologies to Conduct Successful Within-field, On-farm TrialsPerforming randomized replicated trials in row crop field environments has the potential to increase crop production in environmentally sustainable ways. Successful implementation requires an understanding of implement capabilities and sources of potential systematic error, including operator error. Equipment capabilities can be thought of as a series of several critical “links in a chain,” each with implications that propagate downstream. We will... M. Stelford, A. Krmenec |
327. Use of Remotely Measured Potato Canopy Characteristics As Indirect Yield EstimatorsPrediction of potato yield before harvest is important for making agronomic and marketing decisions. Active optical sensors (AOS) are rarely used together with other hand-held instruments for monitoring potato growth, including yield prediction. The aim of the research was to determine the relationship between manually and remotely measured potato crop characteristics throughout the growing season and yield in commercial potato fields. Objective was also to identify crop characteristics that ... S.M. Samborski, J. Szatylowicz, T. Gnatowski, R. Leszczyńska, M. Thornton, O. Walsh |
328. Use of Watering Hole Data As a Decision Support Tool for the Management of a Grazing Herd of CattleEstablish grazing practices would improve the welfare of the animals, allowing them to express more natural behaviours. However, free-range reduces the ability to monitor the animals, thus increase the time needed to intervene in the event of a health problem. To ease the adoption of grazing, farmer would benefit from autonomously collected indicators at pasture that identify abnormal behaviours possibly related to a health problem in a bovine. These indicators must be individualised and coll... J. Plum, B. Quoitin, I. Dufrasne, S. Mahmoudi, F. Lebeau |
329. Using Floral Bract Withering to Identify Green-ripe Pineapples with Deep LearningGreen-ripe pineapples are ideal for extended transportation and storage during summer but are challenging to identify during on-site harvesting. This study introduces a deep learning-based approach using the YOLO-NAS algorithm to detect green-ripe pineapples by analyzing the withering rate of floral bracts at the fruit's base. A high- mounted tracked vehicle, equipped with an Intel D405 depth camera, captures images at a distance of 300–400 mm as it navigates pineapple ridges. The s... S. Chen |
330. Using On-the-Go Soil Sensors to Assess Spatial Variability within the KS Wheat Breeding ProgramIn plant breeding the impacts of genotype by environment interactions and the challenges to quantify these interactions has long been recognized. Both macro and microenvironment variations in precipitation, temperature and soil nutrient availability have been shown to impact breeder selections. Traditionally, breeders mitigate these interactions by evaluating genotype performance across varying environments over multiple years. However, limitations in labor, equipment and seed availably can l... B. Evers, M. Rekhi, G. Hettiarachchi, S. Welch, A. Fritz, P.D. Alderman, J. Poland |
331. Using Prescription Maps for in Field Evaluations of Parameteres Affecting Spraying Accuracy of Self-propelled SprayerWeed presence continues to reemerge year over year, chemical costs continue to increase, and chemical usage continuing to face increasing government oversight, are just a few of the challenges that site-specific weed management intends to address by minimizing wasted application of chemicals and reducing environmental load of active ingredients. Thus, sprayer system manufacturers have developed precision spray systems that allow the individual spray nozzles to be controlled precisely. These s... J. Mayer, P. Flores, J. Stenger |
332. Utilization of UASs to Predict Sugarcane Yields in Louisiana Prior to HarvestOne of the most difficult tasks that both sugarcane producers and processors face every year is estimating the yields of sugarcane fields prior to the start of harvest. This information is needed by processors to determine when the harvest season is to be initiated each year and by producers to decide when each field should be harvested. This is particularly important in Louisiana because the end of the harvest season is often affected by freeze events. These events can severely damage the cr... R.M. Johnson, B. Ramachandran |
333. UWB-IMU System Application and Analysis in Cucumber GreenhousesAccurate and stable positioning is essential for autonomous navigation and environmental monitoring in greenhouse environments. Simultaneous Localization and Mapping (SLAM) is one of the methods to determine the location. This method generally requires a computer and measurement devices such as LiDAR and cameras, making it relatively costly and demanding in terms of deployment conditions. In contrast, ultra- wideband (UWB) is attracting attention as a high-resolution, short-range localization... W. Lin |
334. Variability in Yield Response of Maize to N, P and K Fertilization Towards Site-specific Nutrient Recommendations in Two Maize Belts in TogoSavannah and central regions are the major maize production zones in Togo, but with maize grain yields at a threshold of only 1.5 Mg ha-1. We use a participatory approach to assess the importance of the major three macro elements (N, P and K) for maize cropping in the two regions in order to further allow for site-specific and scalable fertilizer recommendations. Thirty farmers’ fields served as pilot sites, allocated within the two regions to account for spatial variability ... J.M. Sogbedji, M. Lare, A.K. Lotsi, K.A. Amouzou, T. Agneroh |
335. Variable Rate Fertilization in a High-yielding Vineyard of Cv. Trebbiano Romagnolo May Reduce Nitrogen Application and Vigour Variability Without Loss of Crop LoadThe site-specific management of vineyard cultural practices may reduce the spatial variability of vine vigor, contributing to achieve the desired yield and grape composition. In this framework, variable rate fertilization may effectively contribute to reduce the different availability of mineral nutrients between different areas of the vineyard, and so achieving the vine’s aforementioned performances. The present study was aimed to apply a variable rate fertilization in a high... G. Allegro, R. Martelli, G. Valentini, C. Pastore, R. Mazzoleni, F. Pezzi, I. Filippetti, A. Ali |
336. Variable Rate Nitrogen Approach in a Potato-wheat-wheat Cropping SystemNitrogen application in agriculture is a vital process for optimal plant growth and yield outcomes. Different factors such as topography, soil properties, historical yield, and crop stress affect nitrogen (N) needs within a field. Applying variable N within a field could improve precision agriculture. Optimal N management is a system that involves applying a conservative variable base rate at or shortly after planting followed by in-season assessment and, if needed, variable rate application&... E.A. Flint, M. Yost, B.G. Hopkins |
337. Visual Attention and Clinical Scales in Patients with DementiaAttention is a critical indicator in dementia assessment, and its cognitive fluctuation serves as a key metric for evaluating treatment effectiveness. With technological advancements, eye-tracking has emerged as a reliable and non-invasive tool for measuring attentional performance. This study employed the Gazepoint eye-tracker to assess visual attention in 16 patients diagnosed with mild dementia (Clinical Dementia Rating, CDR = 0.5). Two visual response tasks, digit discrimination, and lett... W. Chu |
338. Web Application for Automatic Creation of Thematic Maps and Management Zones - AgDataBox-Fast TrackAgriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture (DA) has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. DA enables information to flow from used agri... J. Aikes junior, E.G. Souza, C. Bazzi, R. Sobjak, A. Hachisuca, A. Gavioli, N. Betzek, K. Schenatto, W. Moreira, E. Mercante, M. Rodrigues |
339. Where to Put Treatments for On-farm ExperimentationOn-farm experimentation has become more and more popular due to advancements in technology. These experiments are not as costly as before, as current machinery can allocate different levels of treatment to specific plots. The main goal of this kind of experiment is to obtain a site-specific nutrient level. The yield behavior is different based on the researcher’s treatment. One unanswered question for on-farm experimentation is how the treatments should be allocated in the first place s... D. Poursina, W. Brorsen |
340. Yield Estimation for Avocado Using Systematic Sampling TechniquesAvocado is a high value crop ranking fourth among the planted fruit species in Chile with more than 32,000 ha. Yield estimation is an important challenge in avocado due to its phenology, the size of the tree, and to the large variability usually observed within the orchards. Due to the practical difficulties to sample the trees we use the following approach: 1) establish a systematic, non-aligned grid with > 20 sampling points (trees)/field, 2) previous to harvest, and ... H.P. Poblete, R.A. Ortega |
341. Yield Mapping in Fruit FarmingDue to the importance of increasing the quantity and quality of world agricultural production, the use of technologies to assist in production processes is essential. Despite this, a timid adoption by precision agriculture (PA) technologies is verified by the Brazilian fruit producers, even though it is one of the segments that had been stood out in recent years in the country's economy. In the PA context, yield maps are rich sources of information, especially by species harvested through... C.L. Bazzi, M.R. Martins, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, A. . Hachisuca, F. Franz |
342. Yolo Strawberry Maturity Classification and Harvest Priority with 3d CameraAccurate harvesting timing is essential to improve crop quality and productivity, and recent advances in agricultural automation have led to the emergence of fruit maturity classification and harvest optimization algorithms for agricultural robots as major technical challenges. This study proposes a pipeline for strawberry object detection, maturity classification, distance estimation, and harvest priority. We train a YOLOv8 detector on an open RGB dataset, and estimate the camera-fruit dista... M. Yang |
343. Yolox-based Monitoring for Humane Poultry SlaughterUsing deep-learning and image-recognition techniques, we built a smart, safe, and humane poultry-slaughter system that raises production efficiency while safeguarding animal welfare. The system centres on a YOLOX object-detection network that classifies each Red-Feather chicken on the processing line as either stunning or unstunning in real time. A total of 1 683 manually labelled images were collected. Of these, 1 268 were reserved for model development and 419 for final testing. The develop... Y. Ho |
344. You Can Not Manage What You Dont MeasureThe problem of variability in soil nutrient analysis has been studied for years by a number of industry experts; unable to decipher and commercialize hyperspectral soil sensing. Many studies have taken years of testing to account for variability thathas a dramatic impacts on precision of recommendations. The main tradeoff we have identified is between accuracy and precision. Large quantities of raw data are requir... K. Fleming, N. Schottle, P. Nagel, G. Koch |