Artificial Neural Networks in Agriculture

• Autres auteurs: Kujawa, Sebastian (Éditeur intellectuel), Niedbała, Gniewko (Éditeur intellectuel)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Sujets: Research & information: general; Biology, life sciences; Technology, engineering, agriculture; 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
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial...