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Moreno, L.A
Vuolo, F
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Authors
Nino, P
Vanino, S
Lupia, F
Altobelli, F
Vuolo, F
Namdarian, I
De Michele, C
Rossi, C
Almeida, S.L
Sysskind, M.N
Moreno, L.A
Felipe dos Santos, A
Lacerda, L
Vellidis, G
Pilcon, C
Orlando Costa Barboza, T
Topics
Remote Sensing Applications in Precision Agriculture
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2012
2024
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1. Applications for Precision Agriculture: the Italian Experience of SIRIUS Project

    This paper reports the results of the project SIRIUS (Sustainable Irrigation water management and River-basin... P. Nino, S. Vanino, F. Lupia, F. Altobelli, F. Vuolo, I. Namdarian, C. De michele

2. Combining Remote Sensing and Machine Learning to Estimate Peanut Photosynthetic Parameters

The 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