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Palabras clave – provistas por la editorial

vineyard; pesticide application; variable rate application; unmanned aerial vehicle; satellite; nanosatellite; monsoon crops; leaf area index; leaf chlorophyll concentration; crop water content; multispectral; hyperspectral; deep learning; forage dry matter yield; high-throughput phenotyping; Brazilian pasture; nitrogen indicator; nitrogen nutrition diagnosis; optical sensor; spectral index; UAV; wheat lodging; lightweight; digital surface model (DSM); winter wheat; fractional order differential; continuous wavelet transform; optimal subset regression; support vector machine; wheat powdery mildew; machine learning; information fusion; remote sensing monitoring; hyperspectral imaging; dimensionality reduction; LDA; PLS; PCA; RandomForest; ReliefF; XGB; Meloidogyne; Solanum tuberosum; soil salinity sensitive parameter; random forest; optimal retrieval model; remote sensing; high throughput phenotyping; UAV/drone; biomass estimation; oats; wheat; yield prediction; random forests; satellite imagery; Normalized Difference Vegetation Index (NDVI); n/a