## 4 Discussion  
Numerous studies show that biometric measurements are highly relevant for predicting the body weight of sheep, and many of them use different techniques such as Multiple Linear Regression (MLR), which is very simple and the most used, and in recent years, machine learning techniques. Nevertheless, there is a lack of studies in the literature using  
Figure 13: Comparison between real weights’ values and estimated weights. (**a**) Comparison using the genetic algorithm; (**b**) Comparison using the cuckoo search algorithm. metaheuristic algorithms, but it is possible to compare the predictive accuracy of our approach based on the \(R^{2}(\%)\) values. In the case of models based on machine learning, [PERSON] and [PERSON] [69] proposed using seven biometric parameters for different models: regression trees (85.4%), linear model (86.1%), support vector machine (89.7%), and random forest (91.6%); it is necessary to mention that they used 131 Balochi sheep from Pakistan, and all of them were males, so all the values obtained were higher than those obtained in our study: 79.23% and 79.98% (Table 5). [PERSON] et al. [70] used only one parameter to estimate the BW; this was the hip width, and they proposed three equations: linear, quadratic, and exponential, obtaining 96%, 96%, and 95%, respectively; in this study, 577 Pelibuey ewe lambs were used. [PERSON] et al. [71] estimated an \(R^{2}(\%)\) value of 64.5% for the Chi-squared Automatic Interaction Detector algorithm (CHAID) in the BW prediction based on nine parameters; for this study, they used Thalli sheep, while [PERSON] et al. [72] estimated a higher \(R^{2}(\%)\) value of 72% for the CHAID algorithm, based on four parameters; in this case, indigenous sheep breeds from Pakistan were used. [PERSON] et al. [73] carried out a study that used images of heifers, and they were processed using computer vision techniques to predict weight using deep neural networks; they obtained two models depending on the view: the side-view model had a coefficient of determination \(R^{2}(\%)\) of 91%, and the top-view model had an \(R^{2}\) of 0.96%. [PERSON] et al. [74] proposed a system for estimating weight by means of 3D zenithal images from 272 lambs, obtaining an \(R^{2}(\%)\) of 86%. Other studies used the Kinect10 sensor to estimate the BW, as was the case of [PERSON] et al. [75]; this study proposed the use of two Kinect10 sensors to estimate the BW of pigs, one for the lateral view and the other for the top section, where they obtained an \(R^{2}(\%)\) of 95%. Our proposal has the advantage of using a single Kinect10, which simplifies the acquisition of the images. Finally, Reference [32] is a review of studies based on CV that were carried out using algorithms based on linear regression and artificial neural networks for weight estimation. However, those studies did not mention the metaheuristic algorithms used in this work. Footnote 10: \(\gamma\) is equal to 9.0 if the girth is less than 65 inches; \(Y\) is equal to 8.5 if the girth is between 65 and 80 inches; \(Y\) is equal to 8.0 if the girth is over 80 inches. \({}^{\text{b}}\)\(L\) is the length of the animal from the point of the shoulder to the pin bone in inches, and \(G\) is the chest girth of the animal in inches. \({}^{\text{c}}\)\(A\) is the body weight (asymptotic), namely the value of a approaches infinity; \(B\) is the scale parameter (the value of the integral constant); \(e\) is the logarithm base (2.718282); \(k\) is the average rate of growth of the body until the animal reaches body maturity; \(M\) is the value of the function in the search for the inflection point (curve shape); \(t\) is the time in units of the month. As shown in Table 7, previous works showed simple empirical formulas or models that cannot take advantage of all the variables that can be measured simultaneously with artificial vision.