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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: |
03/05/2019 |
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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: |
Detection of nutrition deficiencies in plants using proximal images and machine learning: a review. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 162, p. 482-492, July 2019. |
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DOI: |
https://doi.org/10.1016/j.compag.2019.04.035 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. During the last decade, the combination of digital images and machine learning techniques for tackling agricultural problems has been one of the most explored elements of digital farming. In the specific case of proximal images, most efforts have been directed to the detection and classification of plant diseases and crop-damaging pests. Important progress has also been made on the use of close-range images to determine vegetal nutrient status, but because such studies are fewer and more scattered, it is difficult to draw a complete picture on the state of art of this type of research. In this context, a thorough literature search was carried out in order to identify as many relevant investigations on the subject as possible. Every kind of imaging sensor was considered (visible range, multispectral, hyperspectral, chlorophyll fluorescence, etc.), provided that images were captured at close range, thus excluding research using Unmanned Aerial Vehicles (UAVs), airplanes and satellites. A careful analysis of the techniques for detection and classification was carried out and used as basis for an in-depth discussion on the main challenges yet to be overcome. Some directions for future research are also suggested, having as target to increase the practical adoption of this kind of technology. |
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Palavras-Chave: |
Aprendizado de máquina; Image processing; Machine learning; Nutrição de planta; Processamento de imagem; Visão computacional. |
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Thesaurus Nal: |
Computer vision; Image analysis; Plant nutrition. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02121naa a2200241 a 4500
001 2108681
005 2020-01-07
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2019.04.035$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aDetection of nutrition deficiencies in plants using proximal images and machine learning$ba review.$h[electronic resource]
260 $c2019
520 $aAbstract. During the last decade, the combination of digital images and machine learning techniques for tackling agricultural problems has been one of the most explored elements of digital farming. In the specific case of proximal images, most efforts have been directed to the detection and classification of plant diseases and crop-damaging pests. Important progress has also been made on the use of close-range images to determine vegetal nutrient status, but because such studies are fewer and more scattered, it is difficult to draw a complete picture on the state of art of this type of research. In this context, a thorough literature search was carried out in order to identify as many relevant investigations on the subject as possible. Every kind of imaging sensor was considered (visible range, multispectral, hyperspectral, chlorophyll fluorescence, etc.), provided that images were captured at close range, thus excluding research using Unmanned Aerial Vehicles (UAVs), airplanes and satellites. A careful analysis of the techniques for detection and classification was carried out and used as basis for an in-depth discussion on the main challenges yet to be overcome. Some directions for future research are also suggested, having as target to increase the practical adoption of this kind of technology.
650 $aComputer vision
650 $aImage analysis
650 $aPlant nutrition
653 $aAprendizado de máquina
653 $aImage processing
653 $aMachine learning
653 $aNutrição de planta
653 $aProcessamento de imagem
653 $aVisão computacional
773 $tComputers and Electronics in Agriculture$gv. 162, p. 482-492, July 2019.
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