Prediction of Girolando cattle weight by means of body measurements extracted from images
| Autor: | Vanessa Aparecida de Moraes Weber, Fabricio de Lima Weber, Geazy Vilharva Menezes, Adair da Silva Oliveira Junior, Urbano Gomes Pinto de Abreu, Nícolas Alessandro de Souza Belete, Hemerson Pistori, Rodrigo da Costa Gomes |
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| Rok vydání: | 2020 |
| Předmět: |
Dorsum
040301 veterinary sciences biology.animal_breed 0402 animal and dairy science 04 agricultural and veterinary sciences Biology Circumference Body weight SF1-1100 040201 dairy & animal science computer vision mass estimation Girth (geometry) Animal culture 0403 veterinary science machine learning Animal science Girolando cattle cattle Animal Science and Zoology livestock precision |
| Zdroj: | Revista Brasileira de Zootecnia, Vol 49 (2020) Revista Brasileira de Zootecnia, Volume: 49, Article number: e20190110, Published: 23 MAR 2020 Revista Brasileira de Zootecnia v.49 2020 Revista Brasileira de Zootecnia Sociedade Brasileira de Zootecnia (SBZ) instacron:SBZ |
| ISSN: | 1806-9290 1516-3598 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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