Data/hora: 02/06/2024 05:11:28

Biblioteca(s): 	Embrapa Agricultura Digital; Embrapa Pecuária Sudeste.	
Data corrente: 	15/04/2020	
Data da última atualização: 	17/04/2020	
Autoria: 	BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, P. M.; RIBEIRO, A. R. B.	
Afiliação: 	JAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; PATRICIA MENEZES SANTOS, CPPSE; ANDREA ROBERTO BUENO RIBEIRO, UNISA; UNIP.	
Título: 	Counting cattle in UAV images: dealing with clustered animals and animal/background contrast changes.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Sensors, v. 20, n. 7, p. 1-14, Apr. 2020.	
DOI: 	10.3390/s20072126	
Idioma: 	Inglês	
Notas: 	Article number: 2126.	
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.	
Palavras-Chave: 	Canchim breed; Convolutional neural networks; Deep learning mode; Mathematical morphology; Nelore breed; Rede neural convolucional; Redes neurais; Veículo aéreo não tripulado.	
Thesagro: 	Gado Canchim; Gado de Corte; Gado Nelore.	
Thesaurus Nal: 	Neural networks; Unmanned aerial vehicles.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/212350/1/AP-Couting-cattle.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CPPSE - (25108-1) - UPC - AP - DD - PROCI-2020.00033 - BAR - 2020.00079 - DIS
CNPTIA - (20448-1) - UPC - AP - DD -  -  -  - DIS