Artificial Neural Networks in Agriculture
Kujawa, Sebastian
Artificial Neural Networks in Agriculture - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (283 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1579-3 9783036515809 9783036515793
10.3390/books978-3-0365-1579-3 doi
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
Artificial Neural Networks in Agriculture - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (283 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1579-3 9783036515809 9783036515793
10.3390/books978-3-0365-1579-3 doi
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