TY - BOOK ID - 145150872 TI - Artificial Neural Networks in Agriculture AU - Kujawa, Sebastian AU - NiedbaĆa, Gniewko PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Research & information: general KW - Biology, life sciences KW - Technology, engineering, agriculture KW - artificial neural network (ANN) KW - Grain weevil identification KW - neural modelling classification KW - winter wheat KW - grain KW - artificial neural network KW - ferulic acid KW - deoxynivalenol KW - nivalenol KW - MLP network KW - sensitivity analysis KW - precision agriculture KW - machine learning KW - similarity KW - metric KW - memory KW - deep learning KW - plant growth KW - dynamic response KW - root zone temperature KW - dynamic model KW - NARX neural networks KW - hydroponics KW - vegetation indices KW - UAV KW - neural network KW - corn plant density KW - corn canopy cover KW - yield prediction KW - CLQ KW - GA-BPNN KW - GPP-driven spectral model KW - rice phenology KW - EBK KW - correlation filter KW - crop yield prediction KW - hybrid feature extraction KW - recursive feature elimination wrapper KW - artificial neural networks KW - big data KW - classification KW - high-throughput phenotyping KW - modeling KW - predicting KW - time series forecasting KW - soybean KW - food production KW - paddy rice mapping KW - dynamic time warping KW - LSTM KW - weakly supervised learning KW - cropland mapping KW - apparent soil electrical conductivity (ECa) KW - magnetic susceptibility (MS) KW - EM38 KW - neural networks KW - Phoenix dactylifera L. KW - Medjool dates KW - image classification KW - convolutional neural networks KW - transfer learning KW - average degree of coverage KW - coverage unevenness coefficient KW - optimization KW - high-resolution imagery KW - oil palm tree KW - CNN KW - Faster-RCNN KW - image identification KW - agroecology KW - weeds KW - yield gap KW - environment KW - health KW - crop models KW - soil and plant nutrition KW - automated harvesting KW - model application for sustainable agriculture KW - remote sensing for agriculture KW - decision supporting systems KW - neural image analysis KW - artificial neural network (ANN) KW - Grain weevil identification KW - neural modelling classification KW - winter wheat KW - grain KW - artificial neural network KW - ferulic acid KW - deoxynivalenol KW - nivalenol KW - MLP network KW - sensitivity analysis KW - precision agriculture KW - machine learning KW - similarity KW - metric KW - memory KW - deep learning KW - plant growth KW - dynamic response KW - root zone temperature KW - dynamic model KW - NARX neural networks KW - hydroponics KW - vegetation indices KW - UAV KW - neural network KW - corn plant density KW - corn canopy cover KW - yield prediction KW - CLQ KW - GA-BPNN KW - GPP-driven spectral model KW - rice phenology KW - EBK KW - correlation filter KW - crop yield prediction KW - hybrid feature extraction KW - recursive feature elimination wrapper KW - artificial neural networks KW - big data KW - classification KW - high-throughput phenotyping KW - modeling KW - predicting KW - time series forecasting KW - soybean KW - food production KW - paddy rice mapping KW - dynamic time warping KW - LSTM KW - weakly supervised learning KW - cropland mapping KW - apparent soil electrical conductivity (ECa) KW - magnetic susceptibility (MS) KW - EM38 KW - neural networks KW - Phoenix dactylifera L. KW - Medjool dates KW - image classification KW - convolutional neural networks KW - transfer learning KW - average degree of coverage KW - coverage unevenness coefficient KW - optimization KW - high-resolution imagery KW - oil palm tree KW - CNN KW - Faster-RCNN KW - image identification KW - agroecology KW - weeds KW - yield gap KW - environment KW - health KW - crop models KW - soil and plant nutrition KW - automated harvesting KW - model application for sustainable agriculture KW - remote sensing for agriculture KW - decision supporting systems KW - neural image analysis UR - https://www.unicat.be/uniCat?func=search&query=sysid:145150872 AB - 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. ER -