Data/hora: 01/06/2024 07:42:54

Provedor de dados: 	96	
País: 	Brazil	
Título: 	Prediction of Girolando cattle weight by means of body measurements extracted from images	
Autores: 	Weber,Vanessa Aparecida de Moraes; Weber,Fabricio de Lima; Gomes,Rodrigo da Costa; Oliveira Junior,Adair da Silva; Menezes,Geazy Vilharva; Abreu,Urbano Gomes Pinto de; Belete,Nícolas Alessandro de Souza; Pistori,Hemerson.	
Data: 	2020-01-01	
Ano: 	2020	
Palavras-chave: 	Cattle; Computer vision; Livestock precision; Machine learning; Mass estimation.	
Resumo: 	Abstract 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 *   HW I  ( cm ) + 0.01929 *   DA I  (   cm 2 ) + 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.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982020000100800	
Editor: 	Sociedade Brasileira de Zootecnia	
Relação: 	10.37496/rbz4920190110	
Formato: 	text/html	
Fonte: 	Revista Brasileira de Zootecnia  v.49 2020	
Fonte: 	info:eu-repo/semantics/openAccess