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Ish: Integrating the Sentinel-1, Sentinel-2 and topographic data into soybean yield modelling using machine learning
Red and photographic infrared linear combinations for monitoring vegetation
MaqolaOchiq kirish197925 iqtibosABIApplication of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques
Sudhanshu Panda, Daniel P. Ames, Suranjan Panigrahi
MaqolaOchiq kirish20105 iqtibosABIEvaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data
Yingxin Gu, Eric Hunt, Brian Wardlow +3
MaqolaOchiq kirish20083 iqtibosABIPredicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling
Yan Zhao, Andries Potgieter, Miao Zhang +2
MaqolaOchiq kirish20202 iqtibosABIRemote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
Joel Segarra, Ma. Luisa Buchaillot, J. L. Araus +1
MaqolaOchiq kirish20202 iqtibosABISoya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data
Branislav Pejak, Predrag Lugonja, Aleksandar Antić +7
MaqolaOchiq kirish20222 iqtibosABIUnderstanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications
Amanda Veloso, Stéphane Mermoz, Alexandre Bouvet +4
MaqolaOchiq kirish20172 iqtibosABICrop Yield Prediction using Machine Learning Techniques
Ramesh Medar, Vijay S. Rajpurohit, Shweta Shweta
Maqola20192 iqtibosABI