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| Registros recuperados : 7.126 | |
| Registros recuperados : 7.126 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
06/10/2020 |
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Data da última atualização: |
08/10/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
C - 0 |
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Autoria: |
BARBEDO, J. G. A. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
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Título: |
Detecting and classifying pests in crops using proximal images and machine learning: a review. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
AI, v. 1, n. 2, p. 312-328, June 2020. |
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DOI: |
https://doi.org/10.3390/ai1020021 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research. |
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Palavras-Chave: |
Agricultural crops; Aprendizado de máquina; Imagem digital; Imagens digitais; Machine learning; Monitoramento de pragas; Pest detection. |
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Thesagro: |
Infestação; Inseto. |
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Thesaurus NAL: |
Digital images; Insects; Pest monitoring. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/216449/1/AP-Detecting-classifying-2020.pdf
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Marc: |
LEADER 01796naa a2200277 a 4500
001 2125314
005 2020-10-08
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/ai1020021$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aDetecting and classifying pests in crops using proximal images and machine learning$ba review.$h[electronic resource]
260 $c2020
520 $aAbstract: Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital images and machine learning; (2) to provide a unified overview of the research carried out so far, with special emphasis to research gaps that still linger; (3) to propose some possible targets for future research.
650 $aDigital images
650 $aInsects
650 $aPest monitoring
650 $aInfestação
650 $aInseto
653 $aAgricultural crops
653 $aAprendizado de máquina
653 $aImagem digital
653 $aImagens digitais
653 $aMachine learning
653 $aMonitoramento de pragas
653 $aPest detection
773 $tAI$gv. 1, n. 2, p. 312-328, June 2020.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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