LEADER 05125nam 2201369z- 450 001 9910557509803321 005 20240301180214.0 035 $a(CKB)5400000000044458 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76601 035 $a(EXLCZ)995400000000044458 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Neural Networks in Agriculture 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (283 p.) 311 $a3-0365-1580-1 311 $a3-0365-1579-8 330 $aModern 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. 606 $aResearch & information: general$2bicssc 606 $aBiology, life sciences$2bicssc 606 $aTechnology, engineering, agriculture$2bicssc 610 $aartificial neural network (ANN) 610 $aGrain weevil identification 610 $aneural modelling classification 610 $awinter wheat 610 $agrain 610 $aartificial neural network 610 $aferulic acid 610 $adeoxynivalenol 610 $anivalenol 610 $aMLP network 610 $asensitivity analysis 610 $aprecision agriculture 610 $amachine learning 610 $asimilarity 610 $ametric 610 $amemory 610 $adeep learning 610 $aplant growth 610 $adynamic response 610 $aroot zone temperature 610 $adynamic model 610 $aNARX neural networks 610 $ahydroponics 610 $avegetation indices 610 $aUAV 610 $aneural network 610 $acorn plant density 610 $acorn canopy cover 610 $ayield prediction 610 $aCLQ 610 $aGA-BPNN 610 $aGPP-driven spectral model 610 $arice phenology 610 $aEBK 610 $acorrelation filter 610 $acrop yield prediction 610 $ahybrid feature extraction 610 $arecursive feature elimination wrapper 610 $aartificial neural networks 610 $abig data 610 $aclassification 610 $ahigh-throughput phenotyping 610 $amodeling 610 $apredicting 610 $atime series forecasting 610 $asoybean 610 $afood production 610 $apaddy rice mapping 610 $adynamic time warping 610 $aLSTM 610 $aweakly supervised learning 610 $acropland mapping 610 $aapparent soil electrical conductivity (ECa) 610 $amagnetic susceptibility (MS) 610 $aEM38 610 $aneural networks 610 $aPhoenix dactylifera L. 610 $aMedjool dates 610 $aimage classification 610 $aconvolutional neural networks 610 $atransfer learning 610 $aaverage degree of coverage 610 $acoverage unevenness coefficient 610 $aoptimization 610 $ahigh-resolution imagery 610 $aoil palm tree 610 $aCNN 610 $aFaster-RCNN 610 $aimage identification 610 $aagroecology 610 $aweeds 610 $ayield gap 610 $aenvironment 610 $ahealth 610 $acrop models 610 $asoil and plant nutrition 610 $aautomated harvesting 610 $amodel application for sustainable agriculture 610 $aremote sensing for agriculture 610 $adecision supporting systems 610 $aneural image analysis 615 7$aResearch & information: general 615 7$aBiology, life sciences 615 7$aTechnology, engineering, agriculture 700 $aKujawa$b Sebastian$4edt$01324202 702 $aNiedba?a$b Gniewko$4edt 702 $aKujawa$b Sebastian$4oth 702 $aNiedba?a$b Gniewko$4oth 906 $aBOOK 912 $a9910557509803321 996 $aArtificial Neural Networks in Agriculture$93036034 997 $aUNINA