为了更好地解决育肥猪的体重预估问题，本研究通过获取育肥猪在不同生长阶段的图像和质量数据，利用计算机视觉技术将猪的侧视图像进行预处理、颜色特征处理、阈值分割及图像形态学处理，经过推导计算求出猪体的侧视面积，对一维体尺参数、侧视面积与体重进行数据拟合并建立数学模型。研究结果表明：在只考虑体尺单因素的影响时，拟合出的体重与体尺的相关性较小，其平均误差也较大。通过比较逐步回归法与 MLP 神经网络模型发现：MLP神经网络拟合模型相关性最好，相关性R 2可达到0．993，平均相对误差为1．38％，可以很好地保证估测精度，为测量猪的体重提供新的方法。%In order to solve the problem of estimating the weight of the fattening pigs , the image and quality data of pigs was obtained at different growth stages .The research is based on the computer vision technology , the side image of pig will be treated with a series of measure steps which include the processing of image preprocessing , color characters , threshold segmentation and the processing of image pattern , measuring the side area of pigs after computation .Through the analysis of one dimension body size , side area and weight , it can apply the data fitting and establish mathematical model .The results show that the single factor of the body size is considered only , The correlation is lower between body weight and body size , and its average error is larger .Through the comparison of the stepwise regression method and the MLP neural network model , we find that the correlation of the MLP neural network model is the best .The correlation co-efficient is 0 .993 , and the average relative error is 1 .38%.It can assure the precision of evaluations very well , and pro-vide new method for measuring the weight of pigs .
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