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dc.contributor.editorKujawa, Sebastian
dc.contributor.editorNiedbała, Gniewko
dc.date.accessioned2022-01-11T13:36:42Z
dc.date.available2022-01-11T13:36:42Z
dc.date.issued2021
dc.identifierONIX_20220111_9783036515809_336
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/76601
dc.description.abstractModern 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: generalen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciencesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processesen_US
dc.subject.otherartificial neural network (ANN)
dc.subject.otherGrain weevil identification
dc.subject.otherneural modelling classification
dc.subject.otherwinter wheat
dc.subject.othergrain
dc.subject.otherartificial neural network
dc.subject.otherferulic acid
dc.subject.otherdeoxynivalenol
dc.subject.othernivalenol
dc.subject.otherMLP network
dc.subject.othersensitivity analysis
dc.subject.otherprecision agriculture
dc.subject.othermachine learning
dc.subject.othersimilarity
dc.subject.othermetric
dc.subject.othermemory
dc.subject.otherdeep learning
dc.subject.otherplant growth
dc.subject.otherdynamic response
dc.subject.otherroot zone temperature
dc.subject.otherdynamic model
dc.subject.otherNARX neural networks
dc.subject.otherhydroponics
dc.subject.othervegetation indices
dc.subject.otherUAV
dc.subject.otherneural network
dc.subject.othercorn plant density
dc.subject.othercorn canopy cover
dc.subject.otheryield prediction
dc.subject.otherCLQ
dc.subject.otherGA-BPNN
dc.subject.otherGPP-driven spectral model
dc.subject.otherrice phenology
dc.subject.otherEBK
dc.subject.othercorrelation filter
dc.subject.othercrop yield prediction
dc.subject.otherhybrid feature extraction
dc.subject.otherrecursive feature elimination wrapper
dc.subject.otherartificial neural networks
dc.subject.otherbig data
dc.subject.otherclassification
dc.subject.otherhigh-throughput phenotyping
dc.subject.othermodeling
dc.subject.otherpredicting
dc.subject.othertime series forecasting
dc.subject.othersoybean
dc.subject.otherfood production
dc.subject.otherpaddy rice mapping
dc.subject.otherdynamic time warping
dc.subject.otherLSTM
dc.subject.otherweakly supervised learning
dc.subject.othercropland mapping
dc.subject.otherapparent soil electrical conductivity (ECa)
dc.subject.othermagnetic susceptibility (MS)
dc.subject.otherEM38
dc.subject.otherneural networks
dc.subject.otherPhoenix dactylifera L.
dc.subject.otherMedjool dates
dc.subject.otherimage classification
dc.subject.otherconvolutional neural networks
dc.subject.othertransfer learning
dc.subject.otheraverage degree of coverage
dc.subject.othercoverage unevenness coefficient
dc.subject.otheroptimization
dc.subject.otherhigh-resolution imagery
dc.subject.otheroil palm tree
dc.subject.otherCNN
dc.subject.otherFaster-RCNN
dc.subject.otherimage identification
dc.subject.otheragroecology
dc.subject.otherweeds
dc.subject.otheryield gap
dc.subject.otherenvironment
dc.subject.otherhealth
dc.subject.othercrop models
dc.subject.othersoil and plant nutrition
dc.subject.otherautomated harvesting
dc.subject.othermodel application for sustainable agriculture
dc.subject.otherremote sensing for agriculture
dc.subject.otherdecision supporting systems
dc.subject.otherneural image analysis
dc.titleArtificial Neural Networks in Agriculture
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-1579-3
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036515809
oapen.relation.isbn9783036515793
oapen.pages283
oapen.place.publicationBasel, Switzerland


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