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1. Application of Information Technologies in Precision ApicultureApiculture, widely known as beekeeping, is one of the agriculture’s sub directions, where Precision Agriculture (PA) methods can be successfully applied. Adaptation of PA methods and technics into Apiculture, as well as integrating information technologies into beekeeping process can change and improve the beekeepers understanding of bee... E. Stalidzans, A. Zacepins, J. Meitalovs |
2. Evaluation of Photovoltaic Modules at Different Installation Angles and Times of the DaySeveral electricity-consuming components for cooling and heating, illumination, ventilation, and irrigation are used to maintain proper environments of protected crop cultivation facilities. Photovoltaic system is considered as one of the most promising alternative power source for protected cultivation. Effects of environm... S. Chung, J. Kong, Y. Huh, K. Bae, S. Hur, D. Lee, Y. Chae |
3. Climatological Diagnostic Analysis: A Case Study for Parbhani District in Marathwada Region of India... S.N. Pawar, A.K. Gore, G.U. Shinde, M.S. Pendke |
4. Site Specific Costs Concerning Machine Path OrientationComputer algorithms have been created to simulate in advance the orientation/pattern of a machine operation on a field. Undesired impacts were obtained and quantified for these simulations, like: maneuvering and overlap of inputs in headlands; servicing of secondary units; and soil loss by water erosion. While the efforts could minimize the overall costs, they disregard the fact that these costs aren’t uniformly distributed over irregular fields. The cost of a non-productive machine pro... M. Spekken, J.P. Molin, T.L. Romanelli, M.N. Ferraz |
5. Considering Farmers' Situated Expertise in AgriDSS Development to Fostering Sustainable Farming Practices in Precision AgricultureAgriculture is facing immense challenges and sustainable intensification has been presented as a way forward where precision agriculture (PA) plays an important role. More sustainable agriculture needs farmers who embrace situated expertise and can handle changing farming systems. Many agricultural decision support systems (AgriDSS) have been developed to support farm management, but the traditional approach to AgriDSS development is mostly based on knowledge transfer. This has resulted in te... C. Lundström, J. Lindblom |
6. Comparing Adapt-N to Static N Recommendation Approaches for US Maize ProductionLarge temporal and spatial variability in soil N availability leads many farmers across the US to over apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses. Static N recommendation tools are typically promoted in the US, but new dynamic model-based tools allow for more precise and adaptive N recommendations that account for specific production environments and conditions. This study compares two static N recommendation tools... H. Van es, S. Sela, R. Marjerison, B. Moebiu-clune, R. Schindelbeck, D. Moebius-clune |
7. Data Normalization Methods for Definition of Management ZonesThe use of management zones is considered a viable economic alternative for the management of crops due to low cost of adoption as well as economic and environmental benefits. The decision whether or not to normalize the attributes before the grouping process (independent of use) is a problem of methodology, because the attributes have different metric size units, and may influence the result of the clustering process. Thus, the aim of this study was to use a Fuzzy C-Means algorithm to evalua... K. Schenatto, E.G. De souza, C.L. Bazzi, A. Gavioli, N.M. Betzek, H.M. Beneduzzi |
8. EZZone - An Online Tool for Delineating Management ZonesManagement zones are a pillar of Precision Agriculture research. Spatial variability is apparent in all fields, and assessing this variability through measurement devices can lead to better management decisions. The use of Geographic Information Systems for agricultural management is common, especially with management zones. Although many algorithms have been produced in research settings, no online software for management zone delineation exists. This research used a ... G. Vellidis, C. Lowrance, S. Fountas, V. Liakos |
9. Smart Agriculture: A Futuristic Vision of Application of the Internet of Things (IoT) in Brazilian AgricultureWith the economy based on agribusiness, Brazil is an important representative on the world stage in agricultural production, either in terms of quantity or cultivated diversity due to a scenario with vast arable land and favorable climate. There are many crops that are adapteble to soils of the country. Despite the global representation, it is known that the Brazilian agricultural production does not yet have a modern agriculture by restricting the use of new technologies to farmers with bett... C.L. Bazzi, R. Araujo, E.G. Souza, K. Schenatto, A. Gavioli, N.M. Betzek |
10. Towards Data-intensive, More Sustainable Farming: Advances in Predicting Crop Growth and Use of Variable Rate Technology in Arable Crops in the NetherlandsPrecision farming (PF) will contribute to more sustainable agriculture and the global challenge of producing ‘More with less’. It is based on the farm management concept of observing, measuring and responding to inter- and intra-field variability in crops. Computers enabled the use of Farm Management Information Systems (FMIS) and farm and field specific Decision Support Systems (DSS) since mid-1980s. GIS and GNSS allowed since ca. 2000 geo-referencing of data and controlled traff... C. Kempenaar, F. Van evert, T. Been, C. Kocks, K. Westerdijk, S. Nysten |
11. Agronomic Characteristics of Green Corn and Correlations with Productivity for the Establishment of Management Zones in Vale Do Ribeira, SP, BrazilIn Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphi... W.J. Souza, V.S. Akune, S.H. Benez, L.C. Citon, P.H. Nakazawa, A.J. Santana neto |
12. On Farm Studies to Determine Seeding Rate in CornSeeding rate (SDR) is one of the most critical production practices impacting productivity and economic return for corn (Zea mays L.) By changing SDRs in different zones within a field, herein termed as site-specific management, better economic results can be produced as the outcome of reducing SDRs in low productivity areas and increasing SDRs under high-yielding environments, relative to the uniform SDR management performed by the producer. The aim of this study was to analyze yield respons... G. Balboa, S. Varela, I. Ciampitti, S. Duncan, T. Maxwell, D. Shoups, A. Sharda |
13. Closing Yield Gaps with GxExM and Precision AgricultureThere are many challenges to be faced by agriculture if the global population of nine billion people projected for 2050 is to be fed and clothed, especially given the effects of changing climate. A focus on the interactions of genetics x environment x management (GxExM) offers potential for meeting the yield, and environment and economic sustainability goals that are integral to these challenges. The yield gap –defined as the difference between current farmer yields and pote... C. Walthall, J. Hatfield, S. Schneider, M. Vigil |
14. Economics of Gps-enabled Navigation TechnologiesTo address the economic feasibility of global positioning system (GPS) enabled navigation technologies including automated guidance and lightbar, a linear programming model was formulated using data from Midwestern U.S. Corn Belt farms. Five scenarios were compared: (i) a baseline scenario with foam, disk or other visual marker reference, (ii) lightbar navigation with basic GPS availability (+/-3 dm accuracy), (iii) lightbar with satellite subscription correction GPS (+/-1 dm), (iv) automated... T.W. Griffin, D.M. Lambert, J. Lowenberg-deboer |
15. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the TreeImage/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for s... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli |
16. Digital Transformation of Canadian Agri-FoodAgriculture in Canada is on the cusp of a dramatic revolution as a result of the digital transformation of the industry driven by the emergence of tools such as Precision Agri-Food Technologies and the Internet of Things (IoT, a network of interconnected physical devices capable of connecting to the internet). With the expected exponential growth of data from the application of innovative technologies such as IoT by the Canadian Agri-Food industry, Canada has the potential to gain valuable in... K.J. Hand |
17. Optimal Sensor Placement for Field-Wide Estimation of Soil MoistureSoil moisture is one of the most important parameters in precision agriculture. While techniques such as remote sensing seems appropriate for moisture monitoring over large areas, they generally do not offer sufficiently fine resolution for precision work, and there are time restrictions on when the data is available. Moreover, while it is possible to get high resolution-on demand data, but the costs are often prohibitive for most developing countries. Direct ground level measuremen... H. Pourshamsaei, A. Nobakhti |
18. A Case Study Comparing Machine Learning and Vegetation Indices for Assessing Corn Nitrogen Status in an Agricultural Field in MinnesotaCompact hyperspectral sensors compatible with UAV platforms are becoming more readily available. These sensors provide reflectance in narrow spectral bands while covering a wide range of the electromagnetic spectrum. However, because of the narrow spectral bands and wide spectral range, hyperspectral data analysis can benefit greatly from data mining and machine learning techniques to leverage its power. In this study, rainfed corn was grown during the 2017 growing season using four nitrogen ... A. Laacouri, T. Nigon, D. Mulla, C. Yang |
19. Weed Detection Among Crops by Convolutional Neural Networks with Sliding WindowsOne of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neu... K. Kantipudi, C. Lai, C. Min, R.C. Chiang |
20. Changing the Cost of Farming: New Tools for Precision FarmingAccurate prescription maps are essential for effective variable rate fertilizer application. Grid soil sampling has most frequently been used to develop these prescription maps. Past research has indicated several technical and economic limitations associated with this approach. There is a need to keep the number of samples to a minimum while still allowing a reasonable level of map quality. As can be seen, precision agriculture managemen... P. Nagel, K. Fleming |
21. On-Farm Digital Solutions and Their Associated Value to North American FarmersDigital tools and data collection have become standard in a wide variety of present day agricultural operations. An array of digital tools, such as high resolution operational mapping, remote sensing, and farm management software offer solutions to many of the problems in modern agriculture. These technologies and services can, if implemented correctly, provide both immediate and long term agronomic value. A growing number of producers in Ohio and around North America question the proper meth... R. Colley iii, J. Fulton, N. Douridas, K. Port |
22. An Efficient Data Warehouse for Crop Yield PredictionNowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-pr... V.M. Ngo, N. Le-khac, M. Kechadi |
23. AgDataBox – API (Application Programming Interface)E-agricultural is an emerging field focusing in the enhancement of agriculture and rural development through improve in information and data processing. The data-intensive characteristic of these domains is evidenced by the great variety of data to be processed and analyzed. Countrywide estimates rely on maps, spectral images from satellites, and tables with rows for states, regions, municipalities, or farmers. Precision agriculture (PA) relies on maps of within field variability of soil and ... C.L. Bazzi, E.P. Jasse, E.G. Souza, P.S. Magalhães, G.K. Michelon, K. Schenatto, A. Gavioli |
24. Accelerating Precision Agriculture to Decision Agriculture: Enabling Digital Agriculture in AustraliaFor more than two decades, the success of Australia’s agricultural and rural sectors has been supported by the work of the Rural Research and Development Corporations (RDCs). The RDCs are funded by industry and government. For the first time, all fifteen of Australia’s RDC’s have joined forces with the Australian government to design a solution for the use of big data in Australian agriculture. This is the first known example of a nationwide approach for the digital transfor... J. Trindall, R. Rainbow |
25. Pest Detection on UAV Imagery Using a Deep Convolutional Neural NetworkPresently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin |
26. Forecasting Crop Yield Using Multi-Layered, Whole-Farm Data Sets and Machine LearningThe ultimate goal of Precision Agriculture is to improve decision making in the business of farming. Many broadacre farmers now have a number of years of crop yield data for their fields which are often augmented with additional spatial data, such as apparent soil electrical conductivity (ECa), soil gamma radiometrics, terrain attributes and soil sample information. In addition there are now freely available public datasets, such as rainfall, digital soil maps and archives of satellite remote... P. Filippi, E.J. Jones, M. Fajardo, B.M. Whelan, T.F. Bishop |
27. Shared Protocols and Data Template in Agronomic TrialsDue to the overlap of many disciplines and the availability of novel technologies, modern agriculture has become a wide, interdisciplinary endeavor, especially in Precision Agriculture. The adoption of a standard format for reporting field experiments can help researchers to focus on the data rather than on re-formatting and understanding the structure of the data. This paper describes how a European consortium plans to: i) create a “handbook” of protocols for reporting definition... D. Cammarano, D. Drexler, P. Hinsinger, P. Martre, X. Draye, A. Sessitsch, N. Pecchioni, J. Cooper, W. Helga, A. Voicu |
28. Improving the Use of Artificial Neural Networks for Site-Specific Nitrogen FertilizationFor the planning of site-specific nitrogen fertilization, adequate decision rules are needed. Prerequisite for site specific nitrogen fertilization is the site specific forecast of yield. For this the use of artificial neural networks (ANN) has proven particularly interesting. Therefore, ANN based small-scale yield forecasts are realized in order to deviate the economic optimum of fertilization. The basis of yield forecasts with ANN are different site-specific input variables that have presum... J.S. Hauser, P. Wagner |
29. Data Clustering Tools for Understanding Spatial Heterogeneity in Crop Production by Integrating Proximal Soil Sensing and Remote Sensing DataRemote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively hom... M. Saifuzzaman, V.I. Adamchuk, H. Huang, W. Ji, N. Rabe, A. Biswas |
30. Data-Driven Agricultural Machinery Activity Anomaly Detection and ClassificationIn modern agriculture, machinery has become the one of the necessities in providing safe, effective and economical farming operations and logistics. In a typical farming operation, different machines perform different tasks, and sometimes are used together for collaborative work. In such cases, different machines are associated with representative activity patterns, for example, in a harvest scenario, combines move through a field following regular swaths while grain carts follow irregular pa... Y. Wang, A. Balmos, J. Krogmeier, D. Buckmaster |
31. ADAPT: A Rosetta Stone for Agricultural DataModern farming requires increasing amounts of data exchange among hardware and software systems. Precision agriculture technologies were meant to enable growers to have information at their fingertips to keep accurate farm records (and calculate production costs), improve decision-making and promote efficiencies in crop management, enable greater traceability, and so forth. The attainment of these goals has been limited by the plethora of proprietary, incompatible data formats among... D.D. Danford, K.J. Nelson, S.T. Rhea, M.W. Stelford, R. Ferreyra, J.A. Wilson, B.E. Craker |
32. Analyzing Trends for Agricultural Decision Support System Using Twitter DataThe trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmers’ opinions and a method ... S. Jha, D. Saraswat, M.D. Ward |
33. 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 |
34. 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 |
35. 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 |
36. 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 |
37. Geographic Database in Precision Agriculture for the Development of AI ResearchAgriculture 4.0 has profoundly transformed production processes by incorporating technologies such as Precision Agriculture, Artificial Intelligence, the Internet of Things, and telemetry. This evolution has enabled more accurate and timely decision-making in agriculture. In response to this movement, the Precision Agriculture Laboratory (AgriLab) of UTFPR, located in Medianeira, proposes the establishment of a consistent and standardized database. This database is continually updated with su... E.N. Avila, C.L. Bazzi, W.K. Oliveira, K. Schenatto, R. Sobjak, D.M. Rocha |
38. Explainable Neural Network Alternatives for Ai Predictions: Genetic Algorithm Quantitative Association Rule MiningNeural networks in one form or another are common precision agriculture artificial intelligence techniques for making predictions based on data. However, neural networks are computationally intensive to train and to run, and are typically “black-box” models without explainable output. This paper investigates an alternative artificial intelligence prediction technique, genetic algorithm quantitative association rule mining, which creates explainable output with impacts directly qua... M. Everett |
39. Sampling Bumble Bees and Floral Resources Using Deep Learning and UAV ImageryPollinators, essential components of natural and agricultural systems, forage over relatively large spatial scales. This is especially true of large generalist species, like bumble bees. Thus, it can be difficult to estimate the amount and diversity of floral resources available to them. Floral cover and diversity are often estimated over large areas by extrapolation from small scale samples (e.g., a 1-m quadrat) but the accuracy of such estimates can vary depending on the spatial patchiness ... B. Spiesman, I. Grijalva, D. Holthaus, B. Mccornack |
40. Optimizing Nitrogen Application in Global Wheat Production by an Integrated Bayesian and Machine Learning ApproachWheat production plays a pivotal role in global food security, with nitrogen fertilizer application serving as a critical factor. The precise application of nitrogen fertilizer is imperative to maximize wheat yield while avoiding environmental degradation and economic losses resulting from excess or inadequate usage. The integration of Bayesian and machine learning methodologies has gained prominence in the realm of agricultural research. Bayesian and machine learning based methods have great... Z. Liu, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao |
41. Automated Southern Leaf Blight Severity Grading of Corn Leaves in RGB Field ImageryPlant stress phenotyping research has progressively addressed approaches for stress quantification. Deep learning techniques provide a means to develop objective and automated methods for identifying abiotic and biotic stress experienced in an uncontrolled environment by plants comparable to the traditional visual assessment conducted by an expert rater. This work demonstrates a computational pipeline capable of estimating the disease severity caused by southern corn leaf blight in images of ... C. Ottley, M. Kudenov, P. Balint-kurti, R. Dean, C. Williams |
42. Deep Learning for Predicting Yield Temporal Stability from Short Crop RotationsInvestigating the temporal stability of yield in management zones is crucial for both producers and researchers, as it helps in mitigating the adverse impacts of unpredictable disruptions and weather events. The diversification of cropping systems is an approach which leads to reduced variability in yield while improving overall field resilience. In this six-year study spanning from 2016 to 2021, we monitored 40 distinct fields owned by 10 producers situated in Quebec, Canada. These... E. Lord, A.A. Boatswain jacques, A.B. Diallo, M. Khakbazan, A. Cambouris |
43. Enhancing Agricultural Feedback Analysis Through VUI and Deep Learning IntegrationA substantial amount of information relies on consumers, influencing aspects from product adoption to overall satisfaction. Similarly, the agricultural sector is entirely dependent on farmers, who dictate the success of products and highlight associated challenges. Our study aligns with this perspective, recognizing the significance of understanding farmers' needs to assist tractor manufacturing industries. As these industries aim for widespread adoption of their products among farmers, i... S. Kaushal, A. Sharda |
44. An Open Database of Crop Yield Response to Fertilizer Application for SenegalFood security is one of the major global challenges today. Africa is one of the continents with the largest gaps in terms of challenges for food security. In Senegal, about 60% of the population resides in rural areas and the cropping systems are characterized as a low productivity system, low input and in reduced areas, smallholder subsistence systems. Increasing crop productivity would have a positive impact on food security in this country. One of the main factors limiting crop produ... F. Gomez, A. Carcedo, A. Diatta, L. Nagarajan, V. Prasad, Z. Stewart, S. Zingore, I. Ciampitti, P. Djighaly |
45. On Data-driven Crop Yield Modelling, Predicting, and Forecasting and the Common Flaws in Published StudiesThere has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), and abundance of machine learning modelling approaches. This is a particular problem in the field of Precision Agriculture, where many studies will take a crop yield map (or a small number), cr... P. Filippi, T. Bishop, S. Han, I. Rund |
46. Generative Modeling Method Comparison for Class Imbalance CorrectionAn image dataset, for use in object detection of hay bales, with over 6000 images of both good and bad hay bales was collected. Unfortunately, the dataset developed a class imbalance, with more good bale images than bad bales. This dataset class imbalance caused the bad bale class to over train and the good bale class to under train, severely impacting precision, and recall. To correct this imbalance and provide a comparison of differing generative modeling methods; three di... B. Vail, Z. Oster, B. Weinhold |
47. Deep Learning to Estimate Sorghum Yield with Uncrewed Aerial System ImageryIn the face of growing demand for food, feed, and fuel, plant breeders are challenged to accelerate yield potential through quick and efficient cultivar development. Plant breeders often conduct large-scale trials in multiple locations and years to address these goals. Sorghum breeding, integral to these efforts, requires early, accurate, and scalable harvestable yield predictions, traditionally possible only after harvest, which is time-consuming and laborious. This research harnesses high-t... M.A. Bari, A. Bakshi, T. Witt, D. Caragea, K. Jagadish, T. Felderhoff |
48. Machine Vision in Hay Bale ProductionThe goal of this project is to develop a system capable of real-time detection, pass/fail classification, and location tracking of large square hay bales under field conditions. First, a review of past and current methods of object detection was carried out. This led to the selection of the YOLO family of detectors for this project. The image dataset was collected through help from our sponsor, collection of images from the K-STATE research farm, and images collected from th... B. Vail |
49. Design of an Autonomous Ag Platform Capable of Field Scale Data Collection in Support of Artificial IntelligenceThe Pivot+ Array is intended to serve as an innovative, multi-user research platform dedicated to the autonomous monitoring, analysis, and manipulation of crops and inputs at the plant scale, covering extensive areas. It will effectively address many constraints that have historically limited large-scale agricultural sensor and robotic research. This achievement will be made possible by augmenting the well-established center pivot technology, known for its autonomy, with robust power inf... S. Jha, J. Krogmeier, D. Buckmaster, D.J. Love, R.H. Grant, M. Crawford, C. Brinton, C. Wang, D. Cappelleri, A. Balmos |
50. Wheat Spikes Counting Using Density Prediction Convolution Neural NetworkVision-based wheat spikes counting can be valuable for pre-harvest yield estimation for growers and researchers. In this study, wheat spike counting convolutions neural networks were implemented to solve the problem of vision-based wheat yield prediction problem. Encoder-decoder style convolutional neural networks (CNN) were developed with a Global Sum Pooling (GSP) layer as its output layer and trained to produce a density map which predicts the pixelwise wheat spikes density. Thi... C. Liew, S. Pitla |
51. Simultaneously Estimating Crop Biomass and Nutrient Parameters Using UAS Remote Sensing and Multitask LearningRapid and accurate estimation of crop growth status and nutrient levels such as aboveground biomass, nitrogen, phosphorus, and potassium concentrations and uptake is critical with respect to precision agriculture and field-based crop monitoring. Recent developments in Uncrewed Aircraft Systems (UAS) and sensor technologies have enabled the collection of high spatial, spectral, and temporal remote sensing data over large areas at a lower cost. Coupled deep learning-based modeling approaches wi... P. Kovacs, M. Maimaitijiang, B. Millett, L. Dorissant, I. Acharya, U.U. Janjua, K. Dilmurat |
52. Potato Disease Detection Using Laser Speckle Imaging and Deep LearningEarly detection of potato diseases is essential for minimizing crop loss. Implementing advanced imaging techniques can significantly improve the accuracy and efficiency of disease detection in potato crops. Leveraging machine learning algorithms can further enhance the speed and precision of disease identification, enabling timely intervention measures. This work presents a novel potato disease detection technique using whole-potato speckle imaging and deep learning. Laser Speckle Imaging (LS... A.H. Rabia, M.A. Salem |
53. Application of Advanced Soft Computing to Estimate Potato Tuber Yield: a Case Study from Atlantic CanadaThe potato crop plays a crucial role in the economy of Atlantic Canada, particularly in Prince Edward Island and New Brunswick, where it contributes significantly to potato production. To help farmers make informed decisions for sustainable and profitable farming, this study was conducted to examine the variations in potato tuber yield based on thirty soil properties collected over four growing seasons through experimental trials. The study employed an advanced and explainable ensemble model ... Q.U. Zaman, A. Farooque, M. Jamei, T.J. Esau |