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Palabras clave – provistas por la editorial

artificial neural network (ANN); Grain weevil identification; neural modelling classification; winter wheat; grain; artificial neural network; ferulic acid; deoxynivalenol; nivalenol; MLP network; sensitivity analysis; precision agriculture; machine learning; similarity; metric; memory; deep learning; plant growth; dynamic response; root zone temperature; dynamic model; NARX neural networks; hydroponics; vegetation indices; UAV; neural network; corn plant density; corn canopy cover; yield prediction; CLQ; GA-BPNN; GPP-driven spectral model; rice phenology; EBK; correlation filter; crop yield prediction; hybrid feature extraction; recursive feature elimination wrapper; artificial neural networks; big data; classification; high-throughput phenotyping; modeling; predicting; time series forecasting; soybean; food production; paddy rice mapping; dynamic time warping; LSTM; weakly supervised learning; cropland mapping; apparent soil electrical conductivity (ECa); magnetic susceptibility (MS); EM38; neural networks; Phoenix dactylifera L.; Medjool dates; image classification; convolutional neural networks; transfer learning; average degree of coverage; coverage unevenness coefficient; optimization; high-resolution imagery; oil palm tree; CNN; Faster-RCNN; image identification; agroecology; weeds; yield gap; environment; health; crop models; soil and plant nutrition; automated harvesting; model application for sustainable agriculture; remote sensing for agriculture; decision supporting systems; neural image analysis