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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
21/12/2017 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BARBEDO, J. G. A. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
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Título: |
Automatic image-based detection and recognition of plant diseases - a critical view. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 11., 2017, Campinas. Ciência de dados na era da agricultura digital: anais. Campinas: Editora da Unicamp: Embrapa Informática Agropecuária, 2017. |
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Páginas: |
p. 69-77. |
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ISBN: |
978-85-85783-75-4 |
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Idioma: |
Inglês |
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Notas: |
SBIAgro 2017. |
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Conteúdo: |
This paper presents a critical analysis of the current state and future perspectives for the use of digital images applied to plant pathology. The differences between the processes of automatic detection and recognition of diseases in plants are presented, with emphasis on the respective current challenges and difficulties. Some of the limitations intrinsic to the use of digital images for detection and recognition of diseases are discussed. Because some of those limitations are mostly inevitable, they may require the use of ancillary data, which may not always be obtained automatically. As a result, depending on the application, the development of completely automatic diagnosis methods may be unfeasible. Thus, the main objective of this paper is to show that one of the main causes for the low relevance attributed to most algorithms proposed so far is the lack of knowledge by the researchers, especially regarding the real difficulties involved in the diagnosis process. The text concludes showing that significant advancements in this area will only be achieved through careful experimental delineation, realistic objectives, and construction of an image database capable of suitably represent all variations expected to occur within the scope of the algorithm to be developed. |
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Palavras-Chave: |
Diagnóstico de doenças; Fitopatologia; Processamento de imagem. |
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Thesaurus Nal: |
Disease diagnosis; Image analysis; Plant pathology. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/169604/1/Automatic-SBIAgro.pdf
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Marc: |
LEADER 02096nam a2200217 a 4500
001 2083285
005 2020-01-21
008 2017 bl uuuu u00u1 u #d
020 $a978-85-85783-75-4
100 1 $aBARBEDO, J. G. A.
245 $aAutomatic image-based detection and recognition of plant diseases - a critical view.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 11., 2017, Campinas. Ciência de dados na era da agricultura digital: anais. Campinas: Editora da Unicamp: Embrapa Informática Agropecuária$c2017
300 $ap. 69-77.
500 $aSBIAgro 2017.
520 $aThis paper presents a critical analysis of the current state and future perspectives for the use of digital images applied to plant pathology. The differences between the processes of automatic detection and recognition of diseases in plants are presented, with emphasis on the respective current challenges and difficulties. Some of the limitations intrinsic to the use of digital images for detection and recognition of diseases are discussed. Because some of those limitations are mostly inevitable, they may require the use of ancillary data, which may not always be obtained automatically. As a result, depending on the application, the development of completely automatic diagnosis methods may be unfeasible. Thus, the main objective of this paper is to show that one of the main causes for the low relevance attributed to most algorithms proposed so far is the lack of knowledge by the researchers, especially regarding the real difficulties involved in the diagnosis process. The text concludes showing that significant advancements in this area will only be achieved through careful experimental delineation, realistic objectives, and construction of an image database capable of suitably represent all variations expected to occur within the scope of the algorithm to be developed.
650 $aDisease diagnosis
650 $aImage analysis
650 $aPlant pathology
653 $aDiagnóstico de doenças
653 $aFitopatologia
653 $aProcessamento de imagem
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Embrapa Agricultura Digital (CNPTIA) |
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