Authors

Abstract

To explore the application of traditional machine learning model in the intelligent management of pigs, in this paper, the influence of Principal Components Analysis (this method is simply referred to as PCA) pre-treatment on pig face identification with Support Vector Machine (this method is simply referred to as SVM) is studied. By testing method, the kernel functions of two testing schemes, one adopting SVM alone and the other adopting PCA+SVM, were determined to be poly and Radial Basis Function, whose coefficients were 0.03 and 0.01, respectively. With individual identification tests carried out on 10 pigs respectively, the identification accuracy was increased to 88.85% from 83.66% by the improved scheme, also the training time as well as testing time were reduced to 30.1% and 20.97% of the original value in the earlier scheme, respectively. It indicates that PCA pre-treatment had a positive effect on improving the efficiency of individual pig identification with SVM. It provides experimental support for the mobile terminals and embedded application of SVM classifiers.

Abstract in Chinese

摘要 为探索传统机器学习模型在生猪智能管理中的应用，本文研究了PCA前处理对SVM识别猪脸的影响，采用试验方式分别确定仅采用SVM以及PCA+SVM两种试验方案的核函数为poly、RBF，其系数为0.03、0.01，分别对10头生猪进行个体识别试验，优化方案将识别准确率从83.66%提高到88.85%，训练时间和测试时间缩减为原来的30.1%、20.97%，结果表明，使用PCA前处理对采用SVM进行生猪个体识别的效率具有增益作用，可为SVM分类器的移动端和嵌入式应用提供试验支持。