Authors

Abstract

To explore the application of traditional machine learning model in the intelligent management of pigs, in this paper, the influence of the PCA pre-treatment on pig face identification with KNN is studied. With testing method, individual identification test was carried out on 10 different pigs in two testing schemes, in which one adopted KNN alone and the other adopted PCA + KNN, for which the classifier parameter was taken as 3 and 5, respectively. In the optimized scheme, the operating efficiency got significantly increased, also the training time and testing time were reduced to 4.8% and 7% of the original value in the KNN alone scheme, though the accuracy got lowered to a certain extent. With all these factors taken into consideration, PCA pre-treatment is beneficial to individual pig identification with KNN. It can provide experimental support for mobile terminals and embedded application of KNN classifiers.

Abstract in Romanian

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KNN识别猪脸的影响，采用试验方式分别确定仅采用KNN以及PCA+KNN两种试验方案分类器参数值分别为3、5，分别对10头生猪进行个体识别试验，优化方案运行效率显著提升，训练时间和测试时间缩减为原来的4.8%、7%，准确率有一定程度降低，综合考虑，使用PCA前处理对采用KNN进行生猪个体识别具有增益作用，可为KNN分类器的移动端和嵌入式应用提供试验支持。