Reprint

Artificial Neural Networks in Agriculture

Edited by
July 2021
284 pages
  • ISBN978-3-0365-1580-9 (Hardback)
  • ISBN978-3-0365-1579-3 (PDF)

This is a Reprint of the Special Issue Artificial Neural Networks in Agriculture that was published in

Biology & Life Sciences
Engineering
Environmental & Earth Sciences
Summary

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.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; machine learning; recursive feature elimination wrapper; precision agriculture; artificial neural networks; big data; classification; high-throughput phenotyping; modeling; predicting; artificial neural networks; 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; deep learning; transfer learning; average degree of coverage; coverage unevenness coefficient; optimization; neural network; high-resolution imagery; deep learning; oil palm tree; CNN; Faster-RCNN; deep learning; artificial neural networks; image identification; agroecology; weeds; yield gap; environment; health; yield prediction; crop models; soil and plant nutrition; automated harvesting; model application for sustainable agriculture; precision agriculture; remote sensing for agriculture; decision supporting systems; neural image analysis; convolutional neural networks