Classification of apple tree disorders using Convolutional Neural Networks.
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Idioma: | por |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1052112 |
Resumo: | Abstract?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. |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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spelling |
Classification of apple tree disorders using Convolutional Neural Networks.MacieiraRedes neuraisConvolutional Neural NetworksDiseasesNutritional deficienciesDamageApple treesHerbicideMacaAbstract?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.GILMAR RIBEIRO NACHTIGALL, CNPUV.NACHTIGALL, L. G.ARAUJO, R. M.NACHTIGALL, G. R.2016-08-30T11:11:11Z2016-08-30T11:11:11Z2016-08-3020162019-03-08T11:11:11ZArtigo em anais e proceedingsinfo:eu-repo/semantics/publishedVersionIn: 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.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1052112porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2017-08-16T04:17:20Zoai:www.alice.cnptia.embrapa.br:doc/1052112Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542017-08-16T04:17:20Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Classification of apple tree disorders using Convolutional Neural Networks. |
title |
Classification of apple tree disorders using Convolutional Neural Networks. |
spellingShingle |
Classification of apple tree disorders using Convolutional Neural Networks. NACHTIGALL, L. G. Macieira Redes neurais Convolutional Neural Networks Diseases Nutritional deficiencies Damage Apple trees Herbicide Maca |
title_short |
Classification of apple tree disorders using Convolutional Neural Networks. |
title_full |
Classification of apple tree disorders using Convolutional Neural Networks. |
title_fullStr |
Classification of apple tree disorders using Convolutional Neural Networks. |
title_full_unstemmed |
Classification of apple tree disorders using Convolutional Neural Networks. |
title_sort |
Classification of apple tree disorders using Convolutional Neural Networks. |
author |
NACHTIGALL, L. G. |
author_facet |
NACHTIGALL, L. G. ARAUJO, R. M. NACHTIGALL, G. R. |
author_role |
author |
author2 |
ARAUJO, R. M. NACHTIGALL, G. R. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
GILMAR RIBEIRO NACHTIGALL, CNPUV. |
dc.contributor.author.fl_str_mv |
NACHTIGALL, L. G. ARAUJO, R. M. NACHTIGALL, G. R. |
dc.subject.por.fl_str_mv |
Macieira Redes neurais Convolutional Neural Networks Diseases Nutritional deficiencies Damage Apple trees Herbicide Maca |
topic |
Macieira Redes neurais Convolutional Neural Networks Diseases Nutritional deficiencies Damage Apple trees Herbicide Maca |
description |
Abstract?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. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-08-30T11:11:11Z 2016-08-30T11:11:11Z 2016-08-30 2016 2019-03-08T11:11:11Z |
dc.type.driver.fl_str_mv |
Artigo em anais e proceedings |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
In: 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. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1052112 |
identifier_str_mv |
In: 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. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1052112 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
_version_ |
1817695465306587136 |