Proceedings
Authors
| Filter results5 paper(s) found. |
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1. Benchmarking Precision Agriculture Adoption in the United States and BrazilThe United States has long been regarded as a global leader in agricultural technology and productivity. However, rapid advancements in other major producing countries are challenging this position. Brazil, in particular, has paired large-scale crop expansion with accelerated digital transformation, raising important questions about where the United States continues to lead and where it risks losing its competitive edge. Understanding how precision agriculture technologies are being adopted and... J. Colussi, B. Erickson, T. Malone, M. Langemeier, C. Fiechter |
2. Management Zone Delineation for Subsoiling Using Apparent Electrical Conductivity, Elevation, and Soil MoistureSoil compaction is a primary physical factor limiting agricultural crop development. It mechanically impedes root growth, alters soil water dynamics, and consequently affects plant nutrient uptake. In agricultural systems under mechanization or animal trampling, compacted layers are spatially heterogeneous. This necessitates spatial analysis approaches for more precise management decisions, as average values often prove inadequate for site-specific problem resolution. Consequently,... T. Oliveira , G. Ramos Da Silva, E.A. Souza, R.M. Oliveira, B.F. Souza, M.A. Faria |
3. Spatial Delineation of Site-Specific Management Units Using Vegetation Indices in Precision AgriculturePrecision Agriculture has incorporated Remote Sensing as an essential tool for characterizing the spatial variability of agricultural crops. Among the available spectral indices, vegetation indices stand out for their ability to represent vegetative vigor and spatial patterns associated with crop performance. This study aimed to evaluate the spatial stability of spectral indices obtained from a median composite for management zone delineation and to analyze their agreement with a yield map in... L.G. Kern, L. Silveira Pavão, . Müllich, I. Maldaner, L. , J. Sgarbossa, G. Kaefer Seganfredo, E. Rolim Farias Da Silva, M. Silveira Farias |
4. A Machine Learning Framework for Automated Anomaly Detection in Precision Agriculture Geospatial DataModern precision agriculture relies on the analysis of geospatial data generated by a wide range of equipment and sensors. While these datasets are foundational to data-driven management practices, they are often affected by inaccuracies arising from various sources. Existing filter systems, such as Yield Editor and Map Filter, that implement operational (e.g., abrupt changes in speed, speed limits, and removal of maneuvers), global statistical (e.g., observations that are inconsistent with the... Z.C. Komarnisky, F. Hoffmann Silva Karp |
5. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal ImageryAccurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana |