Proceedings
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| Filter results16 paper(s) found. |
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1. The Most Sensitive Growth Stage To Quantify Nitrogen Stress In Sugarcane Using Active Crop Canopy SensorThe use of sensors that allow the application of nitrogen fertilizer at variable rate has been widely used by researchers in many agricultural crops, but without success in sugarcane, probably due to the difficulty of diagnosing the nutritional status of the crop for nitrogen (N). Active crop canopy sensors are based on the principle that the spectral reflectance curve of the leaves are modified by N level. Researchers in USA indicated that in-season N stress in corn can be detected... S.G. Castro, O.T. Kolln, H.S. Nakao, H.C. Franco, O. Braunbeck, P.S. Graziano magalhães, G.M. Sanches |
2. sUAVS Technology For Better Monitoring Crop Status For Winter CanolaThe small-unmanned aircraft vehicles (sUAVS) are currently gaining more popularity in agriculture with uses including identification of weeds and crop production issues, diagnosing nutrient deficiencies, detection of chemical drift, scouting for pests, identification of biotic or abiotic stresses, and prediction of biomass and yield. Research information on the use of sUAVS have been published and conducted in crops such as rice, wheat, and corn, but the development of... I.A. Ciampitti, K. Shroyer, V. Prasad, A. Sharda, M.J. Stamm, H. Wang, K. Price, D. Mangus |
3. Evaluating low-cost Lidar and Active Optical Sensors for pasture and forage biomass assessmentAccurate and reliable assessment of pasture or forage biomass remains one of the key challenges for grazing industries. Livestock managers require accurate estimates of the grassland biomass available over their farm to enable optimal stocking rate decisions. This paper reports on our investigations into the potential application of affordable Lidar (Light Detection and Ranging) systems and Active Optical (reflectance) Sensors (AOS) to estimate pasture biomass. We evaluated the calibration accuracy... M. Trotter, K. Andersson, M. Welch, M. Chau, L. Frizzel, D. Schneider |
4. 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 across... 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 |
5. Developing a Wheat Precision Nitrogen Management Strategy by Combining Satellite Remote Sensing Data and WheatGrow ModelPrecision nitrogen (N) management (PNM) is becoming increasingly popular due to its ability to synchronize crop N demand with soil N supply spatiotemporally. The previous evidence has demonstrated that variable rate fertilization contributes to achieving high yields and high efficiencies. However, PNM at the regional level remains unclear and challenging. This study aims to develop a novel management zone (MZ)-based PNM strategy (MZ-PNM) to optimize the basal and topdressing N rates at the regional... Y. Miao, X. Liu, Y. Tian, Y. Zhu, W. Cao, Q. Cao, X. Chen, Y. Li |
6. 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 data... S. Chen |
7. 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 |
8. 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 detection... S. Chen |
9. 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, identification... S. Chen |
10. 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 fruit,... S. Chen |
11. 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 framework... S. Chen |
12. 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 feasibility... S. Chen |
13. 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, processing... S. Chen |
14. 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 breeding... S. Chen |
15. 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 addresses... S. Chen |
16. 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 by... S. Chen |