Prediction of Girolando cattle weight by means of body measurements extracted from images.

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.

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Main Authors: 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.
Other Authors: 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.
Format: Artigo de periódico biblioteca
Language:English
eng
Published: 2020-03-25
Subjects:Livestock precision, Machine learning, Mass estimation, Cattle, Computer vision,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1121364
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spelling dig-alice-doc-11213642020-03-26T00:45:41Z Prediction of Girolando cattle weight by means of body measurements extracted from images. 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. 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. Livestock precision Machine learning Mass estimation Cattle Computer vision 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. 2020-03-26T00:45:34Z 2020-03-26T00:45:34Z 2020-03-25 2020 2020-04-20T11:11:11Z Artigo de periódico Revista Brasileira de Zootecnia. v. 49, e20190110, 2020. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1121364 en eng openAccess
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language English
eng
topic Livestock precision
Machine learning
Mass estimation
Cattle
Computer vision
Livestock precision
Machine learning
Mass estimation
Cattle
Computer vision
spellingShingle Livestock precision
Machine learning
Mass estimation
Cattle
Computer vision
Livestock precision
Machine learning
Mass estimation
Cattle
Computer vision
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.
Prediction of Girolando cattle weight by means of body measurements extracted from images.
description 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.
author2 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.
author_facet 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.
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.
format Artigo de periódico
topic_facet Livestock precision
Machine learning
Mass estimation
Cattle
Computer vision
author 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.
author_sort WEBER, V. A. de M.
title Prediction of Girolando cattle weight by means of body measurements extracted from images.
title_short Prediction of Girolando cattle weight by means of body measurements extracted from images.
title_full Prediction of Girolando cattle weight by means of body measurements extracted from images.
title_fullStr Prediction of Girolando cattle weight by means of body measurements extracted from images.
title_full_unstemmed Prediction of Girolando cattle weight by means of body measurements extracted from images.
title_sort prediction of girolando cattle weight by means of body measurements extracted from images.
publishDate 2020-03-25
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1121364
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