Data/hora: 09/06/2024 04:30:15

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 21/12/2017
Data da última atualização: | 21/01/2020
Autoria: | BARBEDO, J. G. A.
Afiliação: | JAYME GARCIA ARNAL BARBEDO, CNPTIA.
Título: | Automatic image-based detection and recognition of plant diseases - a critical view.
Ano de publicação: | 2017
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.
Páginas: | p. 69-77.
ISBN: | 978-85-85783-75-4
Idioma: | Inglês
Notas: | SBIAgro 2017.
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.
Palavras-Chave: | Diagnóstico de doenças; Fitopatologia; Processamento de imagem.
Thesaurus Nal: | Disease diagnosis; Image analysis; Plant pathology.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
URL: | http://ainfo.cnptia.embrapa.br/digital/bitstream/item/169604/1/Automatic-SBIAgro.pdf
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (19547-1) - UMT - AA - DD -  -  -  - DIS