Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1052112
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dc.contributor.authorNACHTIGALL, L. G.pt_BR
dc.contributor.authorARAUJO, R. M.pt_BR
dc.contributor.authorNACHTIGALL, G. R.pt_BR
dc.date.accessioned2016-08-30T11:11:11Zpt_BR
dc.date.available2016-08-30T11:11:11Zpt_BR
dc.date.created2016-08-30pt_BR
dc.date.issued2016pt_BR
dc.identifier.citationIn: INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENSE, 28., 2016, San Jose, United States. Anais...San Jose, United States: IEEE, Paper Submission 127, p. 472-476, 2016.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1052112pt_BR
dc.descriptionAbstract?This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of their leaves. This task is fundamental to guarantee a high quality of the resulting yields and is currently largely performed by experts in the field, which can severely limit scale and add to costs. By using a novel data set containing labeled examples consisting of 2539 images from 6 known disorders, we show that trained Convolutional Neural Networks are able to match or outperform experts in this task, achieving a 97.3% accuracy on a hold-out set.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectMacieirapt_BR
dc.subjectRedes neuraispt_BR
dc.subjectConvolutional Neural Networkspt_BR
dc.subjectDiseasespt_BR
dc.subjectNutritional deficienciespt_BR
dc.subjectDamagept_BR
dc.subjectApple treespt_BR
dc.subjectHerbicidept_BR
dc.titleClassification of apple tree disorders using Convolutional Neural Networks.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2019-03-08T11:11:11Zpt_BR
dc.subject.thesagroMacapt_BR
riaa.ainfo.id1052112pt_BR
riaa.ainfo.lastupdate2019-03-08 -03:00:00pt_BR
dc.contributor.institutionGILMAR RIBEIRO NACHTIGALL, CNPUV.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPUV)

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