Camacho-Pérez Enrique, Chay-Canul Alfonso Juventino, Garcia-Guendulain Juan Manuel, Rodríguez-Abreo Omar
Tecnológico Nacional de México/Instituto Tecnológico Superior Progreso, Progreso 97320, Mexico.
Red de Investigación OAC Optimización, Automatización y Control, El Marques 76240, Mexico.
Micromachines (Basel). 2022 Aug 16;13(8):1325. doi: 10.3390/mi13081325.
The Body Weight (BW) of sheep is an important indicator for producers. Genetic management, nutrition, and health activities can benefit from weight monitoring. This article presents a polynomial model with an adjustable degree for estimating the weight of sheep from the biometric parameters of the animal. Computer vision tools were used to measure these parameters, obtaining a margin of error of less than 5%. A polynomial model is proposed after the parameters were obtained, where a coefficient and an unknown exponent go with each biometric variable. Two metaheuristic algorithms determine the values of these constants. The first is the most extended algorithm, the Genetic Algorithm (GA). Subsequently, the Cuckoo Search Algorithm (CSA) has a similar performance to the GA, which indicates that the value obtained by the GA is not a local optimum due to the poor parameter selection in the GA. The results show a Root-Mean-Squared Error (RMSE) of 7.68% for the GA and an RMSE of 7.55% for the CSA, proving the feasibility of the mathematical model for estimating the weight from biometric parameters. The proposed mathematical model, as well as the estimation of the biometric parameters can be easily adapted to an embedded microsystem.
绵羊的体重（BW）对养殖者来说是一个重要指标。遗传管理、营养和健康管理活动都能从体重监测中受益。本文提出了一种次数可调的多项式模型，用于根据绵羊的生物特征参数估计其体重。使用计算机视觉工具测量这些参数，误差幅度小于5%。在获得参数后提出了一个多项式模型，每个生物特征变量都有一个系数和一个未知指数。两种元启发式算法确定这些常数的值。第一种是应用最广泛的算法，即遗传算法（GA）。随后，布谷鸟搜索算法（CSA）的性能与GA相似，这表明GA得到的值并非局部最优，因为GA中参数选择不当。结果表明，GA的均方根误差（RMSE）为7.68%，CSA的RMSE为7.55%，证明了从生物特征参数估计体重的数学模型的可行性。所提出的数学模型以及生物特征参数的估计都可以很容易地应用于嵌入式微系统。
