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| 142. |  | CULIK, M. P.; VENTURA, J. A.; MARTINS, D. dos S. Evaluación de un cebo para el manejo integrado de plaga de hormigas en frutales tropicales en Espiritú Santo, Brasil. / Evaluation of a bait for integrated pest management (IPM) of ants in tropical fruit crops in Espírito Santo, Brazil. In: SIMPOSIO INTERNACIONAL DE FRUTICULTURA TROPICAL Y SUBTROPICAL, 5.; SIMPOSIO INTERNACIONAL DE PIÑA 'FRUTICULTURA 2017', 2017, Cuba. Por una fruticultura competitiva y sostenible : libro de resúmenes / abstract book. Cuba, Instituto de Investigaciones en Fruticultura Tropical, 2017. p. 167 Resumo.| Biblioteca(s): Biblioteca Rui Tendinha. |
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
18/02/2020 |
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Data da última atualização: |
18/02/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ESGARIO, J. G. M.; KROHLING, R. A.; VENTURA, J. A. |
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Afiliação: |
José G. M. Esgario; Renato A. Krohling; 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: |
2020 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 169, fev. 2020. |
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DOI: |
https://doi.org/10.1016/j.compag.2019.105162 |
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Idioma: |
Inglês |
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Conteúdo: |
Biotic stress consists of damage to plants through other living organisms. The 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, increased 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. Computational experiments performed with the proposed system using the ResNet50 architecture obtained an accuracy of for the biotic stress classification and for severity estimation. Moreover, it was found that by classifying only the symptoms, the results were greater than . The experimental results 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. MenosBiotic stress consists of damage to plants through other living organisms. The 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, increased 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. Computational experiments performed with the proposed system using the ResNet50 architecture obtained an accuracy of for the biotic stress classification and for severity estimation. Moreover, it was found that by classifying only the symptoms, the results were greater than . The experimental results 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 cof... Mostrar Tudo |
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Palavras-Chave: |
Biotic stress; Control of biotic; Convolutional neural networks. |
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Categoria do assunto: |
-- |
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URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/123456789/3972/1/Coffee-leaves-stress-ventura.pdf
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Marc: |
LEADER 02151naa a2200193 a 4500
001 1022121
005 2020-02-18
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2019.105162$2DOI
100 1 $aESGARIO, J. G. M.
245 $aDeep learning for classification and severity estimation of coffee leaf biotic stress.$h[electronic resource]
260 $c2020
520 $aBiotic stress consists of damage to plants through other living organisms. The 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, increased 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. Computational experiments performed with the proposed system using the ResNet50 architecture obtained an accuracy of for the biotic stress classification and for severity estimation. Moreover, it was found that by classifying only the symptoms, the results were greater than . The experimental results 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.
653 $aBiotic stress
653 $aControl of biotic
653 $aConvolutional neural networks
700 1 $aKROHLING, R. A.
700 1 $aVENTURA, J. A.
773 $tComputers and Electronics in Agriculture$gv. 169, fev. 2020.
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