摘要
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.
摘要译文
这项研究的目的是分析Girolando牛的体形，以及从它们的图像中提取的测量值，以生成一个模型，以了解哪些测量值可以进一步解释牛的体重。因此，该实验对34头Girolando牛（2头雄性和32头雌性）进行了物理测量，以了解以下特征：心脏周长（HGP），腹部周长，体长，枕骨－坐骨长度，肩高和肩高。此外，这些动物的背部和身体外侧区域的图像允许测量臀部的宽度（HWI），体长，尾巴到脖子的距离，背部的面积（DAI），背部的周长，肩高，身高，臀部的高度，身体的外侧区域，侧面区域的周长和肋骨高度。从图像中提取的测量值经过逐步回归方法和基于回归的机器学习算法。 HGp是相对于体重具有更强正相关的物理量度。在逐步方法中，最终模型的R2为0.70，RMSE为42.52 kg，方程式：重量（kg）= 6.15421 * HW I（cm）+ 0.01929 * DA I（cm 2）+ 70.8388。线性回归和SVM算法获得了最佳结果，然后是随机森林的离散化回归。可以建议本研究中提出的一组规则，通过测量牛的宽度和臀部的面积来估计吉罗兰多牛的体重，相关系数为0.71，相关系数为0.71。
WEBER; Vanessa Aparecida de Moraes et al.. Prediction of Girolando cattle weight by means of body measurements extracted from images.[J]. Revista Brasileira de Zootecnia, 2020,49(49)