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| 112. |  | PEREIRA, A. A.; OLIVEIRA, A. C. B. de. Cultivares. In: SAKIYAMA, N. S.; PRIETO, H. E.; MARTINEZ, H. E. P.; TOMAZ, M. A.; BORÉM, A. (Org.). Café Arábica: do plantio à colheita. 1. ed. Viçosa: Editora UFV, 2015, v.1, p. 24-45.| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 1.522 | |
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Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
18/11/2022 |
|
Data da última atualização: |
18/11/2022 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
BARBEDO, J. G. A. |
|
Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA. |
|
Título: |
A review on the use of computer vision and artificial intelligence for fish recognition, monitoring, and management. |
|
Ano de publicação: |
2022 |
|
Fonte/Imprenta: |
Fishes, v. 7, n. 6, 335, Dec. 2022. |
|
DOI: |
https://doi.org/10.3390/ fishes7060335 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Abstract: Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the current state of the art and to determine the best course of action for new studies. In this context, this article characterizes the current state of the art by identifying the main studies on the subject and briefly describing their approach. In contrast with most previous reviews related to technology applied to fish recognition, monitoring, and management, rather than providing a detailed overview of the techniques being proposed, this work focuses heavily on the main challenges and research gaps that still remain. Emphasis is given to prevalent weaknesses that prevent more widespread use of this type of technology in practical operations under real-world conditions. Some possible solutions and potential directions for future research are suggested, as an effort to bring the techniques developed in the academy closer to meeting the requirements found in practice. |
|
Palavras-Chave: |
Aprendizado profundo; Deep learning; Imagens digitais; Inteligência artificial; Machine learning; Visão computacional. |
|
Thesagro: |
Peixe. |
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Thesaurus NAL: |
Artificial intelligence; Computer vision; Digital images; Fish. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1148425/1/AP-Review-computer-vision-2022.pdf
|
|
Marc: |
LEADER 02016naa a2200265 a 4500
001 2148425
005 2022-11-18
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/ fishes7060335$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aA review on the use of computer vision and artificial intelligence for fish recognition, monitoring, and management.$h[electronic resource]
260 $c2022
520 $aAbstract: Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the current state of the art and to determine the best course of action for new studies. In this context, this article characterizes the current state of the art by identifying the main studies on the subject and briefly describing their approach. In contrast with most previous reviews related to technology applied to fish recognition, monitoring, and management, rather than providing a detailed overview of the techniques being proposed, this work focuses heavily on the main challenges and research gaps that still remain. Emphasis is given to prevalent weaknesses that prevent more widespread use of this type of technology in practical operations under real-world conditions. Some possible solutions and potential directions for future research are suggested, as an effort to bring the techniques developed in the academy closer to meeting the requirements found in practice.
650 $aArtificial intelligence
650 $aComputer vision
650 $aDigital images
650 $aFish
650 $aPeixe
653 $aAprendizado profundo
653 $aDeep learning
653 $aImagens digitais
653 $aInteligência artificial
653 $aMachine learning
653 $aVisão computacional
773 $tFishes$gv. 7, n. 6, 335, Dec. 2022.
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