基于最优二叉决策树分类模型的奶牛运动行为识别  

Cow movement behavior classification based on optimal binary decision-tree classification model

作　　者：王俊 张海洋 赵凯旋 刘刚[2] Wang Jun;Zhang Haiyang;Zhao Kaixuan;Liu Gang(College of Agricultural Equipment Engineering,Henan University of Science and Technology,Luoyang 471003,China;Key Laboratory for Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University,Beijing 100083,China)

机构地区：[1]河南科技大学农业装备工程学院,洛阳471003 [2]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083

出　　处：《农业工程学报》2018年第18期202-210,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金："十三五"国家重点研发计划课题(2018YFD0500705);国家自然科学基金(61771184);河南省科技攻关项目(172102210040);河南省创新型科技人才建设项目(184200510017)

摘　　要：针对奶牛行为分类过程中决策树算法构建主观性强、阈值选取无确定规则,易导致分类精度差的问题,该文提出一种基于最优二叉决策树分类模型的奶牛运动行为识别方法,首先选取描述奶牛腿部三轴加速度数值大小、对称性、陡峭程度、变异程度、不确定性及夹角的24个统计特征量,其次通过构建ROC(receiver operating characteristic,ROC)曲线获得各统计特征量的最佳行为类别分组方式及最优阈值,然后利用信息增益作为最优二叉决策树划分属性的选择标准,最终构建最优二叉决策树分类模型对奶牛运动行为进行分类识别。试验结果表明,该分类模型能够有效区分奶牛的站立、平躺、慢走、快走、站立动作、躺卧动作6种运动行为,平均准确率、平均精度、平均F1值分别为76.47%、76.83%、76.57%,相较传统的ID3(iterative dichotomiser 3,ID3)决策树算法分别高5.71、5.4和5.61个百分点,分别高于K-means聚类算法7.51、8.02和7.77个百分点,优于支持向量机算法6.77、6.72和6.57个百分点。该方法可为提高奶牛行为分类精度提供有效的理论支撑。Changes in behavioral activity are increasingly recognized as a useful indicator of dairy cows’health and welfare.The classifying of changes in behavioral activity can be useful in early detection and prevention of diseases,and monitoring dairy cows’behavioral activity helps farmers to take a comprehensive view of the dairy cows’estrus time.The aim of this study is to automatically measure and distinguish several behavior activities of dairy cows from accelerometer data.The study consists of 2 parts,namely,wireless leg sensor and binary decision-tree algorithm.The wireless leg sensor was designed to collect test data,which integrates microcontroller MSP430F149IMP,tri-axial accelerometer ADXL345,and radio frequency module CC1101 to meet the requirements of accurately collecting data of the acceleration of dairy cows,and long-term reliable transmission of data.The binary decision-tree algorithm was designed to classify the behavior of dairy cows.Firstly,24 statistical features describing the magnitude,symmetry,steepness,variability,uncertainty and angle of the three-axis acceleration of cow legs were selected.Secondly,the best classification behavior category and optimal threshold of each statistical feature were obtained by constructing ROC(receiver operating characteristic)curve.Then the information gain is used as the selection criterion for the split attribute of the binary decision-tree model.Finally,a optimal binary decision tree classification model is constructed to classify and recognize the dairy cow motion behavior.Compared with the traditional binary decision-tree algorithm,the innovation of the algorithm is as follows:Firstly,the ROC curve principle is used to ensure the classification and threshold of each statistical feature to select the local optimal.Then the information gain is used as the split attribute selection standard,and the binary decision-tree classification model is constructed to realize the overall optimal classification of the behavior characteristics of the dairy cows.The resul

关 键 词：数据采集 数据处理 算法 行为分类 三轴加速度计 无线腿部传感器 ROC曲线 二叉决策树 

分 类 号：S24[农业科学—农业电气化与自动化] TP274.2[农业科学—农业工程]