05125nam 2201369z- 450 991055750980332120240301180214.0(CKB)5400000000044458(oapen)https://directory.doabooks.org/handle/20.500.12854/76601(EXLCZ)99540000000004445820202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierArtificial Neural Networks in AgricultureBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (283 p.)3-0365-1580-1 3-0365-1579-8 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.Research & information: generalbicsscBiology, life sciencesbicsscTechnology, engineering, agriculturebicsscartificial neural network (ANN)Grain weevil identificationneural modelling classificationwinter wheatgrainartificial neural networkferulic aciddeoxynivalenolnivalenolMLP networksensitivity analysisprecision agriculturemachine learningsimilaritymetricmemorydeep learningplant growthdynamic responseroot zone temperaturedynamic modelNARX neural networkshydroponicsvegetation indicesUAVneural networkcorn plant densitycorn canopy coveryield predictionCLQGA-BPNNGPP-driven spectral modelrice phenologyEBKcorrelation filtercrop yield predictionhybrid feature extractionrecursive feature elimination wrapperartificial neural networksbig dataclassificationhigh-throughput phenotypingmodelingpredictingtime series forecastingsoybeanfood productionpaddy rice mappingdynamic time warpingLSTMweakly supervised learningcropland mappingapparent soil electrical conductivity (ECa)magnetic susceptibility (MS)EM38neural networksPhoenix dactylifera L.Medjool datesimage classificationconvolutional neural networkstransfer learningaverage degree of coveragecoverage unevenness coefficientoptimizationhigh-resolution imageryoil palm treeCNNFaster-RCNNimage identificationagroecologyweedsyield gapenvironmenthealthcrop modelssoil and plant nutritionautomated harvestingmodel application for sustainable agricultureremote sensing for agriculturedecision supporting systemsneural image analysisResearch & information: generalBiology, life sciencesTechnology, engineering, agricultureKujawa Sebastianedt1324202Niedbała GniewkoedtKujawa SebastianothNiedbała GniewkoothBOOK9910557509803321Artificial Neural Networks in Agriculture3036034UNINA