基于可穿戴传感器组合部署的猪只行为识别研究  

Research on pig behavior recognition based on the combination of wearable sensors

作　　者：何金成[1,2] 杨万林 刘涛 庄俊玮 He Jincheng;Yang Wanlin;Liu Tao;Zhuang Junwei(College of Mechanical and Electrical Engineering,Fujian Agriculture and Forestry University,Fuzhou,350002,China;Engineering Research Center of Modern Agricultural Equipment,Fujian University,Fuzhou,350002,China)

机构地区：[1]福建农林大学机电工程学院,福州市350002 [2]现代农业装备福建省高校工程研究中心,福州市350002

出　　处：《中国农机化学报》2025年第4期42-49,共8页Journal of Chinese Agricultural Mechanization

基　　金：福建省星火项目(2020S0002);福建农林大学科技创新专项(KFA17024A)。

摘　　要：猪只行为监测是实现猪只智能化、精细化养殖的关键技术。采用姿态传感器,设计两种可穿戴设备,分别部署在试验猪的背部和颈部,采集俯卧、侧卧、采食、站立、行走、犬坐和排泄7种日常行为数据。采集的行为数据集有3种,分为单源部署(背部或颈部)和组合部署(背部+颈部)。采集的信号进行小波降噪、数据信号的选取、数据分割、时域特征提取、Relief算法特征选择等数据处理,其中Relief算法特征选择结合BP神经网络和随机森林算法确定特征保留数,以处理好的数据进行输入,建立BP神经网络、随机森林、卷积神经网络和极限学习机4种分类模型,比较各模型性能。结果表明,组合部署的总体准确率明显高于单源部署。BP神经网络、随机森林、卷积神经网络和极限学习机在组合部署的分类准确率分别为90.59%、87.14%、91.67%和82.5%。综合各种评价指标,组合部署的分类模型以CNN卷积神经网络最佳。两个姿态传感器组合部署及融合信息可以很好地对猪只行为进行分类,研究结果对猪只日常行为监测和健康评估有重要意义。Pig behavior monitoring is a key technology to realize intelligent and refined pig breeding.In this study,two wearable devices were designed by using posture sensors,which were deployed on the backs and necks of experimental pigs to collect data on seven daily behaviors,including lying prostrate,side lying,feeding,standing,walking,dog sitting and excretion.There are 3 types of behavioral datasets collected,which are divided into single-source deployment(back or neck)and combined deployment(back+neck).The collected signals were processed by wavelet domain denoising,data signal selection,data segmentation,time-domain feature extraction,and feature selection of Relief algorithm,in which the feature selection of Relief algorithm combined with BP neural network and random forest algorithm to determine the feature retention number k,and then the processed data were sent to four classification models of BP neural network,random forest,convolutional neural network and extreme learning machine for analysis.The overall accuracy of the combined deployment(back+neck)was significantly higher than that of single-source deployment(back or neck).The classification accuracy of behavior by using BPNN,RF,CNN,and ELM in the combined deployment was 90.59%,87.14%,91.67%,and 82.5%.In this study,the characteristics of the duration of each behavior in pigs were analyzed,and the classification effect of different behaviors required different evaluation indexes for analysis.Combining various evaluation indicators,the classification model deployed by combination is the best CNN convolutional neural network.The combined deployment and fusion of the two attitude sensors can classify pig behavior,and the results of this study are of great significance for the daily behavior monitoring and health assessment of pigs.

关 键 词：猪只行为 部署位置 可穿戴设备 姿态传感器 RELIEF算法 卷积神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S828[自动化与计算机技术—计算机科学与技术]