Proceedings
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| Filter results2 paper(s) found. |
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1. Combining Remote Sensing and Machine Learning to Estimate Peanut Photosynthetic ParametersThe environmental conditions in which plants are situated lead to changes in their photosynthetic rate. This alteration can be visualized by pigments (Chlorophyll and Carotenoids), causing changes in plant reflectance. The goal of this study was to evaluate the performance of different Machine Learning (ML) algorithms in estimating fluorescence and foliar pigments in irrigated and rainfed peanut production fields. The experiment was conducted in the southeast of Georgia in the United States in... C. Rossi, S.L. Almeida, M.N. Sysskind, L.A. Moreno, A. Felipe Dos Santos, L. Lacerda, G. Vellidis, C. Pilcon, T. Orlando Costa Barboza |
2. Use of Multispectral UAV Imagery to Monitor Late-Season Defoliation in Peanut Production SystemsReduced leaf area late in the growing season is commonly associated with lower physiological activity in peanut plants, particularly when foliar diseases intensify near harvest. As canopy biomass declines, peg strength may be compromised, increasing the risk of pod loss during digging operations if harvest is delayed. Although canopy biomass decreases become more noticeable near harvest, visual field assessments do not always reflect belowground conditions, making it difficult to determine the... R. Dias Borges, C. Pilcon, A. Felipe Dos Santos, L. Lacerda, C. Rossi |