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Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
18/02/2020 |
Data da última atualização: |
18/02/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
ESGARIO, J. G. M.; KROHLING, R. A.; VENTURA, J. A. |
Afiliação: |
José G. M. Esgario; Renato A. Krohling; Jose Aires Ventura, Incaper. |
Título: |
Deep learning for classification and severity estimation of coffee leaf biotic stress. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 169, fev. 2020. |
DOI: |
https://doi.org/10.1016/j.compag.2019.105162 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Biotic stress; Control of biotic; Convolutional neural networks. |
Categoria do assunto: |
-- |
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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Biblioteca Rui Tendinha (BRT) |
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Registro Completo |
Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
09/01/2015 |
Data da última atualização: |
09/01/2015 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
- - - |
Autoria: |
ARAÚJO, J. B. S.; CARVALHO, G. J. de.; GUIMARÃES. R. J.; CARVALHO, J. G. de. |
Afiliação: |
João Batista Silva Araújo, Incaper; Gabriel José de Carvalho; Rubens José Guimarães; Janice Guedes de Carvalho. |
Título: |
Composto orgânico e biofertilizante na nutrição do cafeeiro em formação no sistema orgânico : teores foliares. |
Ano de publicação: |
2007 |
Fonte/Imprenta: |
Coffee Science, v. 2, n. 1, p. 20-28, dez. 2007. |
Idioma: |
Português |
Notas: |
Título em inglês: Organic compost and bio-fertilizer in coffee nutrition in organic system. |
Conteúdo: |
Com o objetivo de avaliar a adubação de plantio com composto orgânico associado à aplicação foliar de biofertilizante supermagro nos teores foliares de nutrientes do cafeeiro arábica (Coffea arabica L.), instalou-se, em vasos, um experimento em casa-de- vegetação no Setor de Cafeicultura do Departamento de Agricultura da Universidade Federal de Lavras, no período de 15 de março a 4 de outubro de 2003. O delineamento experimental foi em blocos casualizados, em esquema fatorial 5 x 5, mais três tratamentos adicionais (adubação orgânica, orgânica mais mineral e mineral), em quatro repetições e uma planta por parcela. Misturou-se o composto nas doses de 110, 330, 550, 770 e 990 g/vaso a 7 dm3 de solo. Pulverizou-se mensalmente o supermagro a 0%, 3%, 6%, 12% e 24%. Houve interação significativa somente para Mg e B. Houve, com a elevação das doses de composto, aumento dos teores foliares de N, K e Mg e diminuição dos teores de P e Ca, B, Cu, Fe e Mn. O supermagro foi eficiente no fornecimento de Mg, B e Cu.
The experiment was carried out in a greenhouse in the coffee sector of the Agriculture Dept. of the Federal University of Lavras, from March 15th to October 4th, 2003, to evaluate fertilization with an organic compost associated to the leaf application
of the ?supermagro? bio-fertilizer on coffee (Coffea arabica L. cv. Topázio MG-1190) growth and development. A randomized block design with a 5 x 5 + 3 factorial arrangement with four repetitions was used, using one plant per plot. The first factor used was organic compost rate/pot (110, 330, 550, 770 and 990 g per plot). The second was the bio-fertilizer ?supermagro? applied monthly in 0, 3, 6, 12 and 24% concentration. Additional treatments used were organic, organic + mineral and mineral fertilizer soil application. As the compound rate increased, the N, K and Mg leaf content also increased, while P, Ca, B, Cu, Fe and Mn leaf contents decreased. The biofertilizer used was efficient in supplying the plants with Mg, B and Cu. MenosCom o objetivo de avaliar a adubação de plantio com composto orgânico associado à aplicação foliar de biofertilizante supermagro nos teores foliares de nutrientes do cafeeiro arábica (Coffea arabica L.), instalou-se, em vasos, um experimento em casa-de- vegetação no Setor de Cafeicultura do Departamento de Agricultura da Universidade Federal de Lavras, no período de 15 de março a 4 de outubro de 2003. O delineamento experimental foi em blocos casualizados, em esquema fatorial 5 x 5, mais três tratamentos adicionais (adubação orgânica, orgânica mais mineral e mineral), em quatro repetições e uma planta por parcela. Misturou-se o composto nas doses de 110, 330, 550, 770 e 990 g/vaso a 7 dm3 de solo. Pulverizou-se mensalmente o supermagro a 0%, 3%, 6%, 12% e 24%. Houve interação significativa somente para Mg e B. Houve, com a elevação das doses de composto, aumento dos teores foliares de N, K e Mg e diminuição dos teores de P e Ca, B, Cu, Fe e Mn. O supermagro foi eficiente no fornecimento de Mg, B e Cu.
The experiment was carried out in a greenhouse in the coffee sector of the Agriculture Dept. of the Federal University of Lavras, from March 15th to October 4th, 2003, to evaluate fertilization with an organic compost associated to the leaf application
of the ?supermagro? bio-fertilizer on coffee (Coffea arabica L. cv. Topázio MG-1190) growth and development. A randomized block design with a 5 x 5 + 3 factorial arrangement with four repetitions was used, using one plant per plo... Mostrar Tudo |
Palavras-Chave: |
Adubação; Biofertilizante; Café arábica; Café orgânico; Coffea arabica; Fertilizante orgânico; Supermagro. |
Thesaurus NAL: |
Coffea arabica; Organic coffee; Organic fertilizer; Supermagro. |
Categoria do assunto: |
-- |
URL: |
http://biblioteca.incaper.es.gov.br/digital/bitstream/item/506/1/Composto-organico-e-biofertilizante-e-supermagro-na-nutricao-do-cafeeiro-em-formacao-Teores-foliares-JOAO-ARAUJO.pdf
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Marc: |
LEADER 03004naa a2200301 a 4500 001 1004861 005 2015-01-09 008 2007 bl uuuu u00u1 u #d 100 1 $aARAÚJO, J. B. S. 245 $aComposto orgânico e biofertilizante na nutrição do cafeeiro em formação no sistema orgânico$bteores foliares.$h[electronic resource] 260 $c2007 500 $aTítulo em inglês: Organic compost and bio-fertilizer in coffee nutrition in organic system. 520 $aCom o objetivo de avaliar a adubação de plantio com composto orgânico associado à aplicação foliar de biofertilizante supermagro nos teores foliares de nutrientes do cafeeiro arábica (Coffea arabica L.), instalou-se, em vasos, um experimento em casa-de- vegetação no Setor de Cafeicultura do Departamento de Agricultura da Universidade Federal de Lavras, no período de 15 de março a 4 de outubro de 2003. O delineamento experimental foi em blocos casualizados, em esquema fatorial 5 x 5, mais três tratamentos adicionais (adubação orgânica, orgânica mais mineral e mineral), em quatro repetições e uma planta por parcela. Misturou-se o composto nas doses de 110, 330, 550, 770 e 990 g/vaso a 7 dm3 de solo. Pulverizou-se mensalmente o supermagro a 0%, 3%, 6%, 12% e 24%. Houve interação significativa somente para Mg e B. Houve, com a elevação das doses de composto, aumento dos teores foliares de N, K e Mg e diminuição dos teores de P e Ca, B, Cu, Fe e Mn. O supermagro foi eficiente no fornecimento de Mg, B e Cu. The experiment was carried out in a greenhouse in the coffee sector of the Agriculture Dept. of the Federal University of Lavras, from March 15th to October 4th, 2003, to evaluate fertilization with an organic compost associated to the leaf application of the ?supermagro? bio-fertilizer on coffee (Coffea arabica L. cv. Topázio MG-1190) growth and development. A randomized block design with a 5 x 5 + 3 factorial arrangement with four repetitions was used, using one plant per plot. The first factor used was organic compost rate/pot (110, 330, 550, 770 and 990 g per plot). The second was the bio-fertilizer ?supermagro? applied monthly in 0, 3, 6, 12 and 24% concentration. Additional treatments used were organic, organic + mineral and mineral fertilizer soil application. As the compound rate increased, the N, K and Mg leaf content also increased, while P, Ca, B, Cu, Fe and Mn leaf contents decreased. The biofertilizer used was efficient in supplying the plants with Mg, B and Cu. 650 $aCoffea arabica 650 $aOrganic coffee 650 $aOrganic fertilizer 650 $aSupermagro 653 $aAdubação 653 $aBiofertilizante 653 $aCafé arábica 653 $aCafé orgânico 653 $aCoffea arabica 653 $aFertilizante orgânico 653 $aSupermagro 700 1 $aCARVALHO, G. J. de. 700 1 $aGUIMARÃES. R. J. 700 1 $aCARVALHO, J. G. de. 773 $tCoffee Science$gv. 2, n. 1, p. 20-28, dez. 2007.
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