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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
06/08/2019 |
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Data da última atualização: |
06/08/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ESGARIO, J. G. M.; KROHLING, R. A.; VENTURA, J. A. |
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Afiliação: |
José G. M. Esgario, UFES; Renato A. Krohling, UFES; Jose Aires Ventura, Incaper. |
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Título: |
Deep learning for classification and severity estimation of Coffee leaf biotic stress. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
arXiv:1907.11561, p. 1-11, 26 jul 2019. |
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Idioma: |
Inglês |
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Conteúdo: |
Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic
stresses in coffee plantations. |
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Thesaurus NAL: |
Agricultural sustainability; Biotic agents; Biotic stress; Coffee; Pathogens. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/123456789/3652/1/deep-learning-classification-severty-coffee-ventura.pdf
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Marc: |
LEADER 01889naa a2200205 a 4500
001 1021504
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008 2019 bl uuuu u00u1 u #d
100 1 $aESGARIO, J. G. M.
245 $aDeep learning for classification and severity estimation of Coffee leaf biotic stress.$h[electronic resource]
260 $c2019
520 $aBiotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.
650 $aAgricultural sustainability
650 $aBiotic agents
650 $aBiotic stress
650 $aCoffee
650 $aPathogens
700 1 $aKROHLING, R. A.
700 1 $aVENTURA, J. A.
773 $tarXiv:1907.11561, p. 1-11, 26 jul 2019.
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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