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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
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Data corrente: |
15/04/2020 |
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Data da última atualização: |
17/04/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M.; RIBEIRO, A. R. B. |
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Afiliação: |
JAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; PATRICIA MENEZES SANTOS, CPPSE; ANDREA ROBERTO BUENO RIBEIRO, UNISA; UNIP. |
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Título: |
Counting cattle in UAV images: dealing with clustered animals and animal/background contrast changes. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Sensors, v. 20, n. 7, p. 1-14, Apr. 2020. |
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DOI: |
10.3390/s20072126 |
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Idioma: |
Inglês |
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Notas: |
Article number: 2126. |
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Conteúdo: |
Abstract: The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields accuracies over 90% under a wide variety of conditions and backgrounds. |
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Palavras-Chave: |
Canchim breed; Convolutional neural networks; Deep learning mode; Mathematical morphology; Nelore breed; Rede neural convolucional; Redes neurais; Veículo aéreo não tripulado. |
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Thesagro: |
Gado Canchim; Gado de Corte; Gado Nelore. |
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Thesaurus NAL: |
Neural networks; Unmanned aerial vehicles. |
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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/212350/1/AP-Couting-cattle.pdf
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Marc: |
LEADER 01863naa a2200337 a 4500
001 2121664
005 2020-04-17
008 2020 bl uuuu u00u1 u #d
024 7 $a10.3390/s20072126$2DOI
100 1 $aBARBEDO, J. G. A.
245 $aCounting cattle in UAV images$bdealing with clustered animals and animal/background contrast changes.$h[electronic resource]
260 $c2020
500 $aArticle number: 2126.
520 $aAbstract: The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields accuracies over 90% under a wide variety of conditions and backgrounds.
650 $aNeural networks
650 $aUnmanned aerial vehicles
650 $aGado Canchim
650 $aGado de Corte
650 $aGado Nelore
653 $aCanchim breed
653 $aConvolutional neural networks
653 $aDeep learning mode
653 $aMathematical morphology
653 $aNelore breed
653 $aRede neural convolucional
653 $aRedes neurais
653 $aVeículo aéreo não tripulado
700 1 $aKOENIGKAN, L. V.
700 1 $aSANTOS, P. M.
700 1 $aRIBEIRO, A. R. B.
773 $tSensors$gv. 20, n. 7, p. 1-14, Apr. 2020.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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