LEADER 01031nam--2200337---450- 001 990001036960203316 035 $a0103696 035 $aUSA010103696 035 $a(ALEPH)000103696USA01 035 $a0103696 100 $a20020318d1950----km-y0itay0103----ba 101 $ager 102 $aDE 110 $aau--------- 200 1 $aJahrbuch$fAkademie der Wissenschaften und der Literatur 207 $a1950- 210 $aWiesbaden$cVerlag der Akademie der Wissenschaften und der Literatur in Mainz in Kommission bei Franz Steiner Verlag$d1950 215 $av.$d24 cm 606 0 $aPeriodici tedeschi 676 $a053 712 02$aAkademie der Wissenschaften und der Literatur 801 0$aIT$bsalbc$gISBD 912 $a990001036960203316 958 $aUMA$bFONDO JAH$c1950-1978; mancano 1953-1959, 1961 959 $aSE 969 $aUMA 979 $aPATTY$b90$c20020318$lUSA01$h0955 979 $c20020403$lUSA01$h1744 979 $aPATRY$b90$c20040406$lUSA01$h1712 996 $aJahrbuch$9977001 997 $aUNISA LEADER 01911nas--2200433---450- 001 990001056310203316 005 20090304093103.0 011 $a1125-0429 035 $a0105631 035 $aUSA010105631 035 $a(ALEPH)000105631USA01 035 $a0105631 100 $a20020322d--------km-y0itay0103----ba 101 $aita 102 $aIT 110 $aaha-------- 200 1 $aStoria e civiltà 210 $aViterbo$cCentro di studi sulla civiltà comunale 215 $av.$d24 cm 326 $aTrimestrale 606 0 $aCiviltà$xStoria$xPeriodici 676 $a901.9 801 0$aIT$bsalbc$gISBD 912 $a990001056310203316 958 $aUMA$bFondo$c1985-1999; 959 $aSE 969 $aUMA 979 $aPATTY$b90$c20020322$lUSA01$h1532 979 $c20020403$lUSA01$h1745 979 $aPATRY$b90$c20040406$lUSA01$h1713 979 $aVITTORIANA$b90$c20090304$lUSA01$h0931 996 $aSTORIA e civilta$9555501 997 $aUNISA Z30 2$LAdministrative$mISSUE$1UMA$AUMA$3Per III 50$5134965-10$820020705$a2001$b17$c1$f09$FNON Prestabile$hA.17, n.1 (2001)$i20010328$j20010427 Z30 2$lUSA50$LAdministrative$mISSUE$1UMA$AUMA$3Per III 50$5134965-20$820020705$a2001$b17$c2$f09$FNON Prestabile$hA.17, n.2 (2001)$i20010628$j20010728 Z30 2$lUSA50$LAdministrative$mISSUE$1UMA$AUMA$3Per III 50$5134965-30$820020705$a2001$b17$c3$f09$FNON Prestabile$hA.17, n.3 (2001)$i20010928$j20011028 Z30 2$lUSA50$LAdministrative$mISSUE$1UMA$AUMA$3Per III 50$5134965-40$820020705$a2001$b17$c4$f09$FNON Prestabile$hA.17, n.4 (2001)$i20011228$j20020127 Z30 2$lUSA50$LAdministrative$mISSUE$1UMA$AUMA$3Per III 50$5134965-50$820020705$a2002$b18$c1$f09$FNON Prestabile$hA.18, n.1 (2002)$i20020328$j20020427 Z30 2$lUSA50$LAdministrative$mISSUE$1UMA$AUMA$3Per III 50$5134965-60$820020705$a2002$b18$c2$f09$FNON Prestabile$hA.18, n.2 (2002)$i20020628$j20020728 LEADER 05146nam 2201381z- 450 001 9910557509803321 005 20220111 035 $a(CKB)5400000000044458 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76601 035 $a(oapen)doab76601 035 $a(EXLCZ)995400000000044458 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial Neural Networks in Agriculture 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (283 p.) 311 08$a3-0365-1580-1 311 08$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 $aBiology, life sciences$2bicssc 606 $aResearch & information: general$2bicssc 606 $aTechnology, engineering, agriculture$2bicssc 610 $aagroecology 610 $aapparent soil electrical conductivity (ECa) 610 $aartificial neural network 610 $aartificial neural network (ANN) 610 $aartificial neural networks 610 $aautomated harvesting 610 $aaverage degree of coverage 610 $abig data 610 $aclassification 610 $aCLQ 610 $aCNN 610 $aconvolutional neural networks 610 $acorn canopy cover 610 $acorn plant density 610 $acorrelation filter 610 $acoverage unevenness coefficient 610 $acrop models 610 $acrop yield prediction 610 $acropland mapping 610 $adecision supporting systems 610 $adeep learning 610 $adeoxynivalenol 610 $adynamic model 610 $adynamic response 610 $adynamic time warping 610 $aEBK 610 $aEM38 610 $aenvironment 610 $aFaster-RCNN 610 $aferulic acid 610 $afood production 610 $aGA-BPNN 610 $aGPP-driven spectral model 610 $agrain 610 $aGrain weevil identification 610 $ahealth 610 $ahigh-resolution imagery 610 $ahigh-throughput phenotyping 610 $ahybrid feature extraction 610 $ahydroponics 610 $aimage classification 610 $aimage identification 610 $aLSTM 610 $amachine learning 610 $amagnetic susceptibility (MS) 610 $aMedjool dates 610 $amemory 610 $ametric 610 $aMLP network 610 $amodel application for sustainable agriculture 610 $amodeling 610 $aNARX neural networks 610 $aneural image analysis 610 $aneural modelling classification 610 $aneural network 610 $aneural networks 610 $anivalenol 610 $aoil palm tree 610 $aoptimization 610 $apaddy rice mapping 610 $aPhoenix dactylifera L. 610 $aplant growth 610 $aprecision agriculture 610 $apredicting 610 $arecursive feature elimination wrapper 610 $aremote sensing for agriculture 610 $arice phenology 610 $aroot zone temperature 610 $asensitivity analysis 610 $asimilarity 610 $asoil and plant nutrition 610 $asoybean 610 $atime series forecasting 610 $atransfer learning 610 $aUAV 610 $avegetation indices 610 $aweakly supervised learning 610 $aweeds 610 $awinter wheat 610 $ayield gap 610 $ayield prediction 615 7$aBiology, life sciences 615 7$aResearch & information: general 615 7$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