Proceedings
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| Filter results2 paper(s) found. |
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1. Application of Machine Learning Algorithms and Remote Sensing for Predicting Losses in Peanut HarvestingPeanut (Arachis hypogaea L.) is a crop of substantial economic and social relevance in Brazil, particularly in the state of São Paulo, which accounts for the majority of national production and consistently attains high productivity levels. Despite significant advances in agricultural mechanization, harvesting remains one of the most critical phases of peanut production, especially during mechanical digging, a stage in which considerable yield losses frequently occur. These losses are classified... G. Pereira Costa, A.L. Brito Filho, T.C. Oliveira, J. , R.P. Silva |
2. Detection of Weed-Related Anomalies in Sugarcane Fields Using Sentinel-2 ImageryWeed infestation is one of the main causes of yield losses in agricultural systems, particularly in large-scale crops such as sugarcane. Conventional weed management, based on uniform herbicide application, often ignores the spatial variability of infestations, resulting in higher production costs and environmental impacts. In this context, remote sensing and machine learning techniques are recently being used as a solution for automation and precision in crop monitoring. In this study,... R.P. Amaro, F. Amstalden, C. Berro Filho, D.G. Duft |