Artificial Neural Networks in Agriculture (MDPI - Multidisciplinary Digital Publishing Institute, 2021)
Keywords: agroecology; apparent soil electrical conductivity (ECa); artificial neural network; artificial neural network (ANN); artificial neural networks; automated harvesting; average degree of coverage; big data; Biology, life sciences; classification; CLQ; CNN; convolutional neural networks; corn canopy cover; corn plant density; correlation filter; coverage unevenness coefficient; crop models; crop yield prediction; cropland mapping; decision supporting systems; deep learning; deoxynivalenol; dynamic model; dynamic response; dynamic time warping; EBK; EM38; environment; Faster-RCNN; ferulic acid; food production; GA-BPNN; GPP-driven spectral model; grain; Grain weevil identification; health; high-resolution imagery; high-throughput phenotyping; hybrid feature extraction; hydroponics; image classification; image identification; LSTM; machine learning; magnetic susceptibility (MS); Mathematics and Science; Medjool dates; memory; metric; MLP network; model application for sustainable agriculture; modeling; NARX neural networks; neural image analysis; neural modelling classification; neural network; neural networks; nivalenol; oil palm tree; optimization; paddy rice mapping; Phoenix dactylifera L; plant growth; precision agriculture; predicting; recursive feature elimination wrapper; Reference, Information and Interdisciplinary subjects; remote sensing for agriculture; Research and information: general; rice phenology; root zone temperature; sensitivity analysis; similarity; soil and plant nutrition; soybean; Technology, Engineering, Agriculture, Industrial processes; time series forecasting; transfer learning; UAV; vegetation indices; weakly supervised learning; weeds; winter wheat; yield gap; yield prediction