00776nam0-2200265 --450 991068709750332120230503151954.0978-88-495-4976-820230503d2022----kmuy0itay5050 baitaIT 001yy<<L’>>equivoco della privacypersona vs dato personaleVincenzo RicciutoNapoliEdizioni scientifiche italiane2022188 p.23 cmDiritto privato19342.45085823itaRicciuto,Vincenzo<1959->231654ITUNINAREICATUNIMARCBK9910687097503321XIX G 392023/196FGBCFGBCEquivoco della privacy3089880UNINA05146nam 2201381z- 450 991055750980332120220111(CKB)5400000000044458(oapen)https://directory.doabooks.org/handle/20.500.12854/76601(oapen)doab76601(EXLCZ)99540000000004445820202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierArtificial Neural Networks in AgricultureBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online 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.Biology, life sciencesbicsscResearch & information: generalbicsscTechnology, engineering, agriculturebicsscagroecologyapparent soil electrical conductivity (ECa)artificial neural networkartificial neural network (ANN)artificial neural networksautomated harvestingaverage degree of coveragebig dataclassificationCLQCNNconvolutional neural networkscorn canopy covercorn plant densitycorrelation filtercoverage unevenness coefficientcrop modelscrop yield predictioncropland mappingdecision supporting systemsdeep learningdeoxynivalenoldynamic modeldynamic responsedynamic time warpingEBKEM38environmentFaster-RCNNferulic acidfood productionGA-BPNNGPP-driven spectral modelgrainGrain weevil identificationhealthhigh-resolution imageryhigh-throughput phenotypinghybrid feature extractionhydroponicsimage classificationimage identificationLSTMmachine learningmagnetic susceptibility (MS)Medjool datesmemorymetricMLP networkmodel application for sustainable agriculturemodelingNARX neural networksneural image analysisneural modelling classificationneural networkneural networksnivalenoloil palm treeoptimizationpaddy rice mappingPhoenix dactylifera L.plant growthprecision agriculturepredictingrecursive feature elimination wrapperremote sensing for agriculturerice phenologyroot zone temperaturesensitivity analysissimilaritysoil and plant nutritionsoybeantime series forecastingtransfer learningUAVvegetation indicesweakly supervised learningweedswinter wheatyield gapyield predictionBiology, life sciencesResearch & information: generalTechnology, engineering, agricultureKujawa Sebastianedt1324202Niedbała GniewkoedtKujawa SebastianothNiedbała GniewkoothBOOK9910557509803321Artificial Neural Networks in Agriculture3036034UNINA