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Artificial neural networks in agriculture / Sebastian Kujawa (2021)  

Public Titre : Artificial neural networks in agriculture Type de document : Monographie Auteurs : Sebastian Kujawa, Éditeur scientifique ; Gniewko Niedbała, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 283 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-0365-1579-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agriculture [Termes IGN] apprentissage profond [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] couvert végétal [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] détection d'arbres [Termes IGN] Google Earth [Termes IGN] image à haute résolution [Termes IGN] phénologie [Termes IGN] réseau neuronal artificiel [Termes IGN] surveillance agricole Résumé : (éditeur) 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. Note de contenu : 1- Plant and weed identifier robot as an agroecological tool using artificial neural networks for image identification 2- Oil palm tree detection and health classification on high-resolution imagery using deep learning 3- Average degree of coverage and coverage unevenness coefficient as parameters for spraying quality assessment 4- The relationship between soil electrical parameters and compaction of Sandy Clay Loam soil 5- Evaluation of convolutional neural networks’ hyperparameters with transfer learning to determine sorting of Ripe Medjool dates 6- Mapping paddy rice using weakly supervised long short-term memory network with time series sentinel optical and SAR images 7- Time series prediction with artificial neural networks: An analysis using Brazilian soybean production 8- Machine learning for plant breeding and biotechnology 9- A hybrid CFS filter and RF-RFE wrapper-based feature extraction for enhanced agricultural crop yield prediction modeling 10- Crop growth stage GPP-driven spectral model for evaluation of cultivated land quality using GA-BPNN 11- Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles 12- Modeling the dynamic response of plant growth to root zone temperature in hydroponic Chili pepper plant using neural networks 13- ANN-based continual classification in agriculture 14- Application of artificial neural networks to analyze the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat grain 15- Neural visual detection of grain weevil (sitophilus granarius L.) Numéro de notice : 28624 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-1579-3 En ligne : https://doi.org/10.3390/books978-3-0365-1579-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99553