红外热成像下多变量蛋鸡体温预测模型的建立与分析

Establishment and analysis of multivariate body temperature prediction model of laying hens based on infrared thermography

为了探究蛋鸡红外温度和真实温度之间的关系,通过增加解释变量来提高预测模型对两者关系的解释程度,并且消除解释变量之间的共线性,提高预测模型的准确率,试验将红外热成像仪拍摄的蛋鸡红外图像划分为若干感兴趣区域,并且将感兴趣区域的红外温度和环境温度作为反演模型的解释变量,蛋鸡翼下温度作为因变量,建立多元线性回归的蛋鸡体温预测模型;同时,利用岭回归模型对预测模型进行优化,并且在相同的测试集上,与BP神经网络架构下的预测模型进行对比,分析不同模型下的平均相对误差。结果表明:多元线性回归模型的平均相对误差为1.06%,岭回归模型的平均相对误差为0.71%,BP神经网络模型的平均相对误差为0.28%。在蛋鸡体温处于41~42℃时,三种模型在误差允许的范围内都能准确预测出蛋鸡真实体温;在蛋鸡体温超出42℃时,岭回归模型和BP神经网络模型的误差大多分布在0~1%之间,满足精度要求,其中BP神经网络模型的预测准确率最好,而多元线性回归模型与真实温度相差较大,无法准确预测蛋鸡真实体温。说明与多元线性回归模型相比,岭回归模型和BP神经网络模型可以消除解释变量之间的共线性,提高模型的稳定性,使预测结果更加准确。

In order to explore the relationship between the infrared temperature and the true temperature of the laying hens,the explanatory variables were added to improve the degree of interpretation of the relationship between the two by the prediction model,and the collinearity between the explanatory variables was eliminated to improve the accuracy of the prediction model.In the experiment,the infrared image of the laying hen taken by the infrared thermal imager was divided into several regions of interest;the infrared temperature and ambient temperature of the region of interest were taken as the explanatory variables of the inversion model,and the temperature under the wings of the laying hens was used as the dependent variable to establish a multiple linear regression prediction model of laying hens body temperature.At the same time,ridge regression was used to optimize the prediction model,and on the same test set,it was compared with the prediction model under the BP neural network architecture to analyze the average relative error of different models.The results showed that the average relative error of multiple linear regression model was 1.06%;the average relative error of the ridge regression model was 0.71%,and the average relative error of the BP neural network model was 0.28%.When the body temperature of the laying hen was between 41℃and 42℃,the three models could accurately predict the real body temperature of the laying hens within the allowable error range.When the body temperature of the laying hens exceeded 42℃,the error between the ridge regression model and the BP neural network model were mostly distributed between 0-1%,which met the accuracy requirements.The prediction accuracy of BP neural network model was the best,while the multiple linear regression model differed greatly from the real temperature,so it could not accurately predict the real temperature of laying hens.Compared with the multiple linear regression model,the ridge regression model and the BP neural network model could eliminate the collinearity between explanatory variables,improve the stability of the model,and make the prediction results more accurate.