Data/hora: 23/09/2024 13:44:49

Biblioteca(s): 	Embrapa Gado de Corte; Embrapa Pantanal.	
Data corrente: 	25/03/2020	
Data da última atualização: 	20/04/2020	
Autoria: 	WEBER, V. A. de M.; WEBER, F. de L.; GOMES, R. da C.; OLIVEIRA JUNIOR, A. da S.; MENEZES, G. V.; ABREU, U. G. P. de; BELETE, N. A. de S.; PISTORI, H.	
Afiliação: 	Vanessa Aparecida de Moraes Weber, Universidade Católica Dom Bosco - UCDB; Fabricio de Lima Weber, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; RODRIGO DA COSTA GOMES, CNPGC; Adair da Silva Oliveira Junior, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; Geazy Vilharva Menezes, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; URBANO GOMES PINTO DE ABREU, CPAP; Nícolas Alessandro de Souza Belete, Universidade Católica Dom Bosco - UCDB; Hemerson Pistori, Universidade Católica Dom Bosco - UCDB.	
Título: 	Prediction of Girolando cattle weight by means of body measurements extracted from images.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Revista Brasileira de Zootecnia. v. 49, e20190110, 2020.	
Idioma: 	Inglês
Português	
Conteúdo: 	The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HWI (cm) + 0.01929 * DAI (cm2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images.	
Palavras-Chave: 	Livestock precision; Machine learning; Mass estimation.	
Thesagro: 	Gado de Corte; Gado Gir; Morfologia Animal; Peso.	
Thesaurus Nal: 	Beef cattle; Body weight; Cattle; Computer vision; Gir (cattle breed); Livestock production.	
Categoria do assunto: 	--
L Ciência Animal e Produtos de Origem Animal	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/212007/1/Prediction-of-girolando-cattle.pdf	
Registro original: 	Embrapa Gado de Corte (CNPGC)	
Exemplares: 	CPAP - (60532-1) - UPC - AP - DD -  -  -  - DIS
CNPGC - (17536-1) - UPC - AP - DD -  -  -  - DIS