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

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 (HG P ), 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 (HW I ), body length, tail distance to the neck, dorsum area (DA I ), 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 HG p 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.

Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

“…The group of beef cattle were numbered from 1 to 10 in ascending order, from which 24,16,19,26,14,22,20,11,18, and 12 sets of point clouds were collected, respectively, resulting in 182 sets of point clouds. The body dimensions for each animal were also manually measured for comparison.…”

“…Similarly, Kwon et al [13] proposed an iterative offset-based method to establish a point cloud mesh model of pigs, where the widest regions of the foreleg, hind leg, and middle torso were manually determined, measuring the body dimensions based on geometric features. Weber et al [14] measured the hip width, body length, and shoulders from beef cattle images using the ImageJ [15] software based on manual markers, through which the body weight was well estimated. Guo et al [16] developed a semi-automatic and open-access body dimension measurement tool to deal with point cloud data of cattle and pigs sharing similar characteristics, which helped to extract several parameters, such as the height, chest girth, and body length, by labelling points manually, resulting in measurement errors of 2-7%.…”

Türkiye'nin Kuzeydoğusunda In-situ Şartlar Altında Yerli Zavot Sığırının Fenotipik Karakterizasyonu

“…Despite some real determination and detection styles (Mwambene et al, 2014;Hossain et al, 2017;Sakar et al, 2020) in body weight determined for adult cattle, mathematical equality-based estimates still have preserved its actuality. Estimate models for Girolando cattle in Brasil by Weber et al, (2020), Zebu and their crosses with Guzerat or Bos Taurus in Senegal by Tebug et al, (2018), Fleckvieh (Simmental crossbred) heifers in Germany by Willeke and Dürsch (2002), Lagune cattle in Southern Benin by Comlan et al, (2017), crossbred beef cattle in Turkey by Ozkaya and Bozkurt (2009) and Holstein-Friesian cattle in Tayland by Mekparyup et al, (2013) was confirmed the body weight estimate models for adult cattle using in the present study. This character was calculated based on different regression models in crossbred cows in Turkey by Ozkaya and Bozkurt (2009), Girolando cattle in Brasil by Weber et al, (2020), indigenous Baggara bulls in Sudan by Abdelhadi and Babiker (2009), Zebu cattle in Senegal by Tebug et al, (2018) as 460, 473, 266 and 302 kg respectively.…”

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

“…In Refs. [ 10 , 11 ], only three attributes of sheep—body length, body height, and chest girth—were provided for predicting sheep. The weight-prediction methods were computed by a multiple linear regression analysis and generalized linear model.…”