Artificial Neural Networks in Agriculture

• Autore: Kujawa Sebastian
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (283 p.)
• Soggetto topico: Biology, life sciences; Research & information: general; Technology, engineering, agriculture
• Soggetto non controllato: agroecology; apparent soil electrical conductivity (ECa); artificial neural network; artificial neural network (ANN); artificial neural networks; automated harvesting; average degree of coverage; big data; 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); 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; remote sensing for agriculture; rice phenology; root zone temperature; sensitivity analysis; similarity; soil and plant nutrition; soybean; time series forecasting; transfer learning; UAV; vegetation indices; weakly supervised learning; weeds; winter wheat; yield gap; yield prediction
• Persona (resp. second.): NiedbałaGniewko; KujawaSebastian
• Sommario/riassunto: 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 intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.
• Titolo autorizzato: Artificial Neural Networks in Agriculture
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557509803321
• Lo trovi qui: Univ. Federico II