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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
03/10/2018 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BARBEDO, J. G. A. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
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Título: |
Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 153, p. 46-53, Oct. 2018. |
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DOI: |
https://doi.org/10.1016/j.compag.2018.08.013 |
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Idioma: |
Inglês |
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Conteúdo: |
The problem of automatic recognition of plant diseases has been historically based on conventional machine learning techniques such as Support Vector Machines, Multilayer Perceptron Neural Networks and Decision Trees. However, the prevailing approach has shifted to the application of deep learning concepts, with focus on Convolutional Neural Networks (CNNs). In general, this kind of technique requires large datasets containing a wide variety of conditions to work properly. This is an important limitation, given the many challenges involved in the construction of a suitable image database. In this context, this study investigates how the size and variety of the datasets impact the effectiveness of deep learning techniques applied to plant pathology. This investigation was based on an image database containing 12 plant species, each presenting very different characteristics in terms of number of samples, number of diseases and variety of conditions. Experimental results indicate that while the technical constraints linked to automatic plant disease classification have been largely overcome, the use of limited image datasets for training brings many undesirable consequences that still prevent the effective dissemination of this type of technology. |
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Palavras-Chave: |
Deep neural nets; Disease classification; Image database; Image processing; Processamento de imagem; Redes neurais. |
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Thesagro: |
Doença de Planta. |
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Thesaurus Nal: |
Neural networks; Plant diseases and disorders. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02096naa a2200241 a 4500
001 2096832
005 2020-01-07
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2018.08.013$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aImpact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification.$h[electronic resource]
260 $c2018
520 $aThe problem of automatic recognition of plant diseases has been historically based on conventional machine learning techniques such as Support Vector Machines, Multilayer Perceptron Neural Networks and Decision Trees. However, the prevailing approach has shifted to the application of deep learning concepts, with focus on Convolutional Neural Networks (CNNs). In general, this kind of technique requires large datasets containing a wide variety of conditions to work properly. This is an important limitation, given the many challenges involved in the construction of a suitable image database. In this context, this study investigates how the size and variety of the datasets impact the effectiveness of deep learning techniques applied to plant pathology. This investigation was based on an image database containing 12 plant species, each presenting very different characteristics in terms of number of samples, number of diseases and variety of conditions. Experimental results indicate that while the technical constraints linked to automatic plant disease classification have been largely overcome, the use of limited image datasets for training brings many undesirable consequences that still prevent the effective dissemination of this type of technology.
650 $aNeural networks
650 $aPlant diseases and disorders
650 $aDoença de Planta
653 $aDeep neural nets
653 $aDisease classification
653 $aImage database
653 $aImage processing
653 $aProcessamento de imagem
653 $aRedes neurais
773 $tComputers and Electronics in Agriculture$gv. 153, p. 46-53, Oct. 2018.
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