| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pecuária Sudeste. |
|
Data corrente: |
25/01/2023 |
|
Data da última atualização: |
26/01/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
WEBER, F. de L.; WEBER, V. A. de M; MORAES, P. H. de; MATSUBARA, E. T.; PAIVA, D. M. B.; GOMES, M. de N. B.; OLIVEIRA, L. O. F. de; MEDEIROS, S. R. de; CAGNIN, M. I. |
|
Afiliação: |
FABRICIO DE LIMA WEBER, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; VANESSA APARECIDA DE MORAES WEBER, UNIVERSIDADE ESTADUAL DE MATO GROSSO DO SUL; PEDRO HENRIQUE DE MORAES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDSON TAKASHI MATSUBARA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; DÉBORA MARIA BARROSO PAIVA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; MARINA DE NADAI BONIN GOMES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; LUIZ ORCIRIO FIALHO DE OLIVEIRA, CNPGC; SERGIO RAPOSO DE MEDEIROS, CPPSE; MARIA ISTELA CAGNIN, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL. |
|
Título: |
Counting cattle in UAV images using convolutional neural network. |
|
Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 29, article 100900, 2023. |
|
ISSN: |
2352-9385 |
|
DOI: |
https://doi.org/10.1016/j.rsase.2022.100900 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Determining the number of cattle in countries with the most extensive livestock and large pastures is difficult requires a lot of time of the farm workforce and is stressful to the animals. Counting cattle in an agile way using tools that can automatically perform this task would be very useful for herd conferences and farm management. The proposed solution is to count cattle through images acquired by Unmanned Aerial Vehicles (UAVs). This allows faster acquisition of the number of cattle in a given area so management tasks can be more accurately done and, allowing better interventions towards technical improvements. Thus, models of architectures from Convolutional Neural Networks (CNN) to YOLOv4 and YOLOv5 models (X, L, M, and S) were used for comparison. In order to evaluate the efficiencies of these solutions for the bovine counting, 878 images were acquired through flights of 20, 40, 80, and 100 m high. YOLOv4 obtained a precision of 0.90, and the YOLOv5 architectures (X, L, M, and S) were 0.98, 0.96, 0.93, and 0.96, respectively. In conclusion, the use of CNN to identify and count cattle from UAV images is a viable solution. |
|
Thesagro: |
Gado Nelore; Sanidade Animal; Sensoriamento Remoto. |
|
Thesaurus Nal: |
Animal welfare; Cattle; Livestock; Neural networks; Remote sensing. |
|
Categoria do assunto: |
--
L Ciência Animal e Produtos de Origem Animal |
|
Marc: |
LEADER 02144naa a2200337 a 4500
001 2151218
005 2023-01-26
008 2023 bl uuuu u00u1 u #d
022 $a2352-9385
024 7 $ahttps://doi.org/10.1016/j.rsase.2022.100900$2DOI
100 1 $aWEBER, F. de L.
245 $aCounting cattle in UAV images using convolutional neural network.$h[electronic resource]
260 $c2023
520 $aDetermining the number of cattle in countries with the most extensive livestock and large pastures is difficult requires a lot of time of the farm workforce and is stressful to the animals. Counting cattle in an agile way using tools that can automatically perform this task would be very useful for herd conferences and farm management. The proposed solution is to count cattle through images acquired by Unmanned Aerial Vehicles (UAVs). This allows faster acquisition of the number of cattle in a given area so management tasks can be more accurately done and, allowing better interventions towards technical improvements. Thus, models of architectures from Convolutional Neural Networks (CNN) to YOLOv4 and YOLOv5 models (X, L, M, and S) were used for comparison. In order to evaluate the efficiencies of these solutions for the bovine counting, 878 images were acquired through flights of 20, 40, 80, and 100 m high. YOLOv4 obtained a precision of 0.90, and the YOLOv5 architectures (X, L, M, and S) were 0.98, 0.96, 0.93, and 0.96, respectively. In conclusion, the use of CNN to identify and count cattle from UAV images is a viable solution.
650 $aAnimal welfare
650 $aCattle
650 $aLivestock
650 $aNeural networks
650 $aRemote sensing
650 $aGado Nelore
650 $aSanidade Animal
650 $aSensoriamento Remoto
700 1 $aWEBER, V. A. de M
700 1 $aMORAES, P. H. de
700 1 $aMATSUBARA, E. T.
700 1 $aPAIVA, D. M. B.
700 1 $aGOMES, M. de N. B.
700 1 $aOLIVEIRA, L. O. F. de
700 1 $aMEDEIROS, S. R. de
700 1 $aCAGNIN, M. I.
773 $tRemote Sensing Applications: Society and Environment$gv. 29, article 100900, 2023.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Gado de Corte (CNPGC) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 346 | |
| Registros recuperados : 346 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
