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| 4. |  | NACHTIGALL, L. G.; ARAUJO, R. M.; NACHTIGALL, G. R. Classification of apple tree disorders using Convolutional Neural Networks. 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.| Biblioteca(s): Embrapa Uva e Vinho. |
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| 12. | | SANTOS, H. P. dos; FONTANELI, R. S.; CASTRO, R. L. de; SANTI, A.; POSSEBOM, T.; ARAUJO, R. M. de. Desempenho econômico de sistemas de manejo de solo envolvendo a cultura de trigo. In: REUNIÃO DA COMISSÃO BRASILEIRA DE PESQUISA DE TRIGO E TRITICALE, 12., 2018, Passo Fundo. Atas e resumos... Passo Fundo: Projeto Passo Fundo, 2019. Ecologia, Fisiologia e Práticas Culturais, p. 83-87.| Biblioteca(s): Embrapa Trigo. |
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| 13. | | SANTOS, H. P. dos; FONTANELI, R. S.; SANTI, A.; CASTRO, R. L. de; POSSEBOM, T.; ARAUJO, R. M. de. Desempenho econômico de sistemas de rotação de culturas envolvendo a cultura de trigo. In: REUNIÃO DA COMISSÃO BRASILEIRA DE PESQUISA DE TRIGO E TRITICALE, 12., 2018, Passo Fundo. Atas e resumos... Passo Fundo: Projeto Passo Fundo, 2019. Ecologia, Fisiologia e Práticas Culturais, p. 88-92.| Biblioteca(s): Embrapa Trigo. |
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| 14. | | ARAÚJO, R. M. de; FONTANELI, R. S.; SANTOS, H. P. dos; MANFRON, A. C. A.; KLEIN, A. P.; ZENI, M. Desempenho produtivo e de valor nutritivo de forrageiras perenes no final verão e outono no norte do Rio Grande do Sul. In: MOSTRA DE INICIAÇÃO CIENTÍFICA, 13.; MOSTRA DE PÓS-GRADUAÇÃO DA EMBRAPA TRIGO, 10., 2018, Passo Fundo. Resumos... Brasília, DF: Embrapa, 2018. p. 27 Resumos Graduação Pibic/CNPq.| Biblioteca(s): Embrapa Trigo. |
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| 15. | | MARTINS, V. L. C.; MARTINS-DA-SILVA, R. C. V.; ARAÚJO, R. M. de; SILVA, M. J. S. da. Levantamento e caracterização de tipos nomenclaturais do Herbário da Embrapa Amazônia Oriental. In: CONGRESSO NACIONAL DE BOTÂNICA, 59.; REUNIÃO NORDESTINA DE BOTÂNICA, 31.; CONGRESSO LATINOAMERICANO Y DEL CARIBE DE CACTÁCEAS Y OTRAS SUCULENTAS, 4.; CONGRESS OF INTERNATIONAL ORGANIZATION FOR SUCULENT PLANT STUDY, 30., 2008, Natal. Atualidades, desafios e perspectivas da botânica no Brasil: resumos. Natal: UFERSA: UFRN: SBB, 2008. 33/41| Biblioteca(s): Embrapa Amazônia Oriental. |
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Registro Completo
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Biblioteca(s): |
Embrapa Uva e Vinho. |
|
Data corrente: |
08/12/2017 |
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Data da última atualização: |
30/04/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 5 |
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Autoria: |
NACHTIGALL, L. G.; ARAUJO, R. M.; NACHTIGALL, G. R. |
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Afiliação: |
Lucas Garcia Nachtigall, Center for Technological Advancement, Federal University of Pelotas, Pelotas, Brazil; Ricardo Matsumura Araujo, Center for Technological Advancement, Federal University of Pelotas, Pelotas, Brazil; GILMAR RIBEIRO NACHTIGALL, CNPUV. |
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Título: |
Use of images of leaves and fruits of apple trees for automatic identification of symptoms of diseases and nutritional disorders. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
International Journal of Monitoring and Surveillance Technologies Research, v. 5, n. 2, p. 1-14, April/June 2017. |
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Idioma: |
Inglês |
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Conteúdo: |
Rapid diagnosis ofsymptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The resultsshowed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way. Keywords Apple, Apple Disorders, Artificial Intelligence, Automatic Disease Identification, Classifications, Convolutional Neural Networks, Disorders, Machine Learning |
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Palavras-Chave: |
Apple; Apple Disorders; Automatic Disease Identification; Classifications; Convolutional Neural; Macieira. |
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Thesagro: |
Doença; Doença de planta; Maçã. |
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Thesaurus NAL: |
Artificial Intelligence. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/168523/1/Use-of-Images-of-Leaves-and-Fruits-of-Apple-Trees-for-Automatic-Identification-of-Symptoms-of-Diseases-and-Nutritional-Disorders.pdf
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Marc: |
LEADER 02098naa a2200265 a 4500
001 2081978
005 2019-04-30
008 2017 bl uuuu u00u1 u #d
100 1 $aNACHTIGALL, L. G.
245 $aUse of images of leaves and fruits of apple trees for automatic identification of symptoms of diseases and nutritional disorders.$h[electronic resource]
260 $c2017
520 $aRapid diagnosis ofsymptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The resultsshowed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way. Keywords Apple, Apple Disorders, Artificial Intelligence, Automatic Disease Identification, Classifications, Convolutional Neural Networks, Disorders, Machine Learning
650 $aArtificial Intelligence
650 $aDoença
650 $aDoença de planta
650 $aMaçã
653 $aApple
653 $aApple Disorders
653 $aAutomatic Disease Identification
653 $aClassifications
653 $aConvolutional Neural
653 $aMacieira
700 1 $aARAUJO, R. M.
700 1 $aNACHTIGALL, G. R.
773 $tInternational Journal of Monitoring and Surveillance Technologies Research$gv. 5, n. 2, p. 1-14, April/June 2017.
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