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| Filter results7 paper(s) found. |
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1. Precision Management of Cattle Feedlot WasteOpen-lot cattle feeding operations face challenges in control of nutrient runoff, leaching, and gaseous emissions. This report investigates the use of precision management of saline soils as found on 1) feedlot surfaces and on a 2) vegetative treatment area (VTA) utilized to control feedlot runoff. An electromagnetic induction soil conductivity meter was used to collect apparent soil electrical conductivity (ECa) from a feedlot pen and a research VTA at the U.S. Meat Animal Research Center, Clay... |
2. Evaluation of the Effect of Different Herbicide Treatments by Using UAV in Maise (Zea mays L.) Cultivation – First Experiences in a Long-term Experiment at Széchenyi István University, HungaryAs part of the Green Deal, the European Union has set a goal to reduce the use of chemical pesticides by 50 percent until 2030. To achieve this goal, in addition to reducing the amount of pesticide used, attention must also be paid to monitoring the temporal and spatial effects of pesticides on weeds during the cultivation of various crops. Hence, Syngenta Ltd., collaborating with researchers, aimed to monitor the effect of five different types of herbicides by UAV in two tillage treatments (CN... I.M. Kulmany, B. Horváth, G. Kukorelli, S. Zsebő, D. Stencinger, Z. Borbás, R. Pecze, L. Bede, Z. Varga, A. Kósa, G. Pinke, Z.K. Hashim, G. Hegedűs, J.A. Abdinoor, G.S. Agampodi |
3. Quantifying Prediction Uncertainty in Field-scale Soil Maps Generated by Machine Learning.Field-scale maps of soil properties are a key component of precision agriculture, as they are routinely used as inputs for variable-rate fertilization, zone delineation, and site-specific management. While machine learning models have substantially improved the accuracy of spatial predictions, uncertainty associated with these predictions is often ignored, limiting the reliability of soil maps as decision-support tools. Quantifying prediction uncertainty is essential not only to assess map quality,... F. García Seleme, P. Paccioretti, M. Balzarini, M. Córdoba |
4. Application for Pixel-level Segmentation and Quantification of Lignified Fibers (Sclerenchyma) and Parenchyma in Microscopic Images of Sugarcane CulmsQuantifying lignified tissues in sugarcane culms (Saccharum spp.) is essential for anatomical characterizations and for inferences related to biomass quality and the potential uses of plant material. Conventional methods may require specific laboratory procedures and manual steps in digital analysis, increasing processing time and reliance on skilled operators. In this context, computer vision techniques applied to microscopic images constitute an accessible and reproducible alternative,... G.F. Rubio, J.F. Rubio, L.E. Pereira, J.R. Marques, A.R. Tech, M.F. Logli |
5. Temporal Stability of Management Zones Derived from Vegetation Indices and Yield Data in Contrasting Production SystemsThe delineation of management zones is a central component of site-specific crop management in precision agriculture. However, the temporal stability of zones derived from different data sources remains a key challenge, particularly when vegetation indices and yield data are combined across multiple seasons. This study evaluates the temporal stability of management zones delineated using vegetation indices and yield data derived from long-term commercial field datasets. The proposed methodology... |
6. Statistical Mean Comparisons in Unreplicated Yield Trials with Georeferenced DataPrecision agriculture technologies have enabled the collection of large volumes of georeferenced yield data within experimental fields. In practice, many on-farm experiments (OFE) are implemented as large unreplicated strips or field zones containing numerous observations within each zone. The lack of replication prevents the use of classical statistical models for comparing zone means. Although many yield observations are available per zone, spatial autocorrelation violates independence assumptions... M. Córdoba, P. Paccioretti, M. Balzarini |
7. Detection of latrine areas in equine paddocks using drones and computer visionEquines can exhibit behaviors that are harmful to the soil, such as spatial segregation, which is caused by their selective grazing pattern. This species may choose its feeding areas based on vegetation structural characteristics, such as forage density, leaf availability, and stage of maturity (which are perceived through their tactile receptors). When present daily, this natural behavior can impair soil health, as spatial segregation within paddocks intensifies and latrine (dung) areas form.... |