基于支持向量机分类模型的奶牛行为识别方法
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摘要
针对奶牛行为监测耗费人力、监测精度低等问题,以无线传输颈环获得的数据为研究对象,提出了一种基于萤火虫算法优化支持向量机参数的奶牛行为分类方法。该方法利用萤火虫寻优算法优化支持向量机的参数,达到较高的分类精度。实验结果表明,无线传输颈环能够实时采集和传输奶牛颈部活动信息,并能有效区分不同奶牛的进食、反刍、饮水3种行为,适用性有了较大提高,其中,分类精度、灵敏度和准确率平均值分别达到97. 28%、97. 03%、98. 02%。对比常规的支持向量机算法,本文方法对同一奶牛的分类精度、灵敏度、准确率平均值分别提高了13. 39、28. 2、18. 8个百分点;不同奶牛的分类精度、灵敏度、准确率平均值分别提高了0. 74、2. 24、2. 12个百分点。本文研究结果可为奶牛异常行为检测、疾病智能化预警提供技术支持。
Aiming at the problems of manpower behavior spend and low monitoring accuracy of dairy cows,a cow behavior classification method was proposed based on that firefly algorithm to optimize support vector machine parameters by taking advantage of the data which obtained by wireless transmission neck ring. The method optimized the parameters of the support vector machine by using the firefly optimization algorithm to achieve the optimal classification accuracy. The experimental results showed that the wireless transmission collars can collect and transmit the cow neck activity information simultaneously. And the algorithm could effectively distinguish the three behaviors of different cows' feeding,ruminating and drinking. The applicability was greatly improved. Among them,the optimal precision,sensitivity and accuracy rate were 97. 28%,97. 03% and 98. 02%,respectively. Compared with the conventional support vector machine algorithm,using the method proposed,the classification accuracy,sensitivity and accuracy of the same cow were increased by 13. 39,28. 2 and 18. 8 percentage points,respectively; the classification accuracy,sensitivity and accuracy of different dairy cows were increased by 0. 74,2. 24 and 2. 12 percentage points,respectively. The research results can provide technical support for further research on abnormal behavior detection and intelligent early warning of diseases in dairy cows.
Aiming at the problems of manpower behavior spend and low monitoring accuracy of dairy cows,a cow behavior classification method was proposed based on that firefly algorithm to optimize support vector machine parameters by taking advantage of the data which obtained by wireless transmission neck ring. The method optimized the parameters of the support vector machine by using the firefly optimization algorithm to achieve the optimal classification accuracy. The experimental results showed that the wireless transmission collars can collect and transmit the cow neck activity information simultaneously. And the algorithm could effectively distinguish the three behaviors of different cows' feeding,ruminating and drinking. The applicability was greatly improved. Among them,the optimal precision,sensitivity and accuracy rate were 97. 28%,97. 03% and 98. 02%,respectively. Compared with the conventional support vector machine algorithm,using the method proposed,the classification accuracy,sensitivity and accuracy of the same cow were increased by 13. 39,28. 2 and 18. 8 percentage points,respectively; the classification accuracy,sensitivity and accuracy of different dairy cows were increased by 0. 74,2. 24 and 2. 12 percentage points,respectively. The research results can provide technical support for further research on abnormal behavior detection and intelligent early warning of diseases in dairy cows.
引文
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[13]LEROY C,WALTON J,LEBLANC S.Estrous detection intensity and accuracy and optimal timing of insemination with automated activity monitors for dairy cows[J].Journal of Dairy Science,2018,101(2):1638-1647.
[14]彭琛,韩立新.基于深度强化学习的计步方法[J].计算机与现代化,2019(1):63-68.PENG Chen,HAN Lixin.Deep reinforcement learning for step counting approach[J].Computer and Modernization,2019(1):63-68.(in Chinese)
[15]顾静秋,王志海,高荣华,等.基于融合图像与运动量的奶牛行为识别方法[J].农业机械学报,2017,48(6):145-151.GU Jingqiu,WANG Zhihai,GAO Ronghua,et al.Recognition method of cow behavior based on combination of image and activities[J].Transactions of the Chinese Society for Agricultural Mechinery,2017,48(6):145-151.(in Chinese)
[16]高云,郭继亮,黎煊,等.基于深度学习的群猪图像实例分割方法[J].农业机械学报,2019,50(4):179-187.GAO Yun,GUO Jiliang,LI Xuan,et al.Instance-level segmentation method for group pig images based on deep learning[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(4):179-187.(in Chinese)
[17]侯云涛,蔡晓华,吴泽全,等.奶牛行为特征识别方法的研究与实现---基于支持向量机[J].农机化研究,2018,40(8):36-41.HOU Yuntao,CAI Xiaohua,WU Zequan,et al.Research and implementation of cattle behavior character recognition method[J].Journal of Agricultural Mechanization Research,2018,40(8):36-41.(in Chinese)
[18]强茂山,张东成,江汉臣.基于加速度传感器的建筑工人施工行为识别方法[J].清华大学学报(自然科学版),2017,57(12):1338-1344.QIANG Maoshan,ZHANG Dongcheng,JIANG Hanchen.Recognizing construction worker activities based on accelerometers[J].Journal of Tsinghua University(Science and Technology),2017,57(12):1338-1344.(in Chinese)
[19]SMITH D,DUTTA R,HELLICAR A,et al.Bag of class posteriors,a new multivariate time series classifier applied to animal behaviour identification[J].Expert Systems with Applications,2015,42(7):3774-3784.
[20]王俊,张海洋,赵凯旋,等.基于最优二叉决策树分类模型的奶牛运动行为识别[J].农业工程学报,2018,34(18):202-210.WANG Jun,ZHANG Haiyang,ZHAO Kaixuan,et al.Cow movement behavior classification based on optimal binary decisiontree classification model[J].Transactions of the CSAE,2018,34(18):202-210.(in Chinese)
[21]尹令,刘财兴,洪添胜,等.基于无线传感器网络的奶牛行为特征监测系统设计[J].农业工程学报,2010,26(3):203-208.YIN Ling,LIU Caixing,HONG Tiansheng,et al.Design of system for monitoring dairy cattle's behavioral features based on wireless sensor networks[J].Transactions of the CSAE,2010,26(3):203-208.(in Chinese)
[22]SHAHRIAR M,SMITH D,RAHMAN A,et al.Detecting heat events in dairy cows using accelerometers and unsupervised learning[J].Computers and Electronics in Agriculture,2016,128:20-26.
[23]陈春玲,杨天娇,郭雷,等.支持向量机在舍饲肉牛反刍行为分析中的应用[J].沈阳农业大学学报,2017,48(6):751-756.CHEN Chunling,YANG Tianjiao,GUO Lei,et al.Application of support vector machine in analysis of ruminant behavior of beef cattle[J].Journal of Shenyang Agricultural University,2017,48(6):751-756.(in Chinese)
[24]尹令,洪添胜,刘迎湖,等.基于无线传感器网络支持向量机奶牛行为特征识别[J].传感技术学报,2011,24(3):458-462.YIN Ling,HONG Tiansheng,LIU Yinghu,et al.Cow behavioral features recognition using binary decision tree support vector machines based on wireless sensor network[J].Chinese Journal of Sensors and Actuators,2011,24(3):458-462.(in Chinese)
[2]ARCIDIACONO C,PORTO S,MANCINO M,et al.Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data[J].Computers and Electronics in Agriculture,2017,134:124-134.
[3]宋怀波,牛满堂,姬存慧,等.基于视频分析的多目标奶牛反刍行为监测[J].农业工程学报,2018,34(18):211-218.SONG Huaibo,NIU Mantang,JI Cunhui,et al.Monitoring of multi-target cow ruminant behavior based on video analysis technology[J].Transactions of the CSAE,2018,34(18):211-218.(in Chinese)
[4]李蓝祁,刘江静,陈晓丽,等.奶牛发情期活动量变化规律研究[J].畜牧兽医学报,2018,49(7):1387-1393.LI Lanqi,LIU Jiangjing,CHEN Xiaoli,et al.Study on the activity alteration of dairy cow during estrus[J].Acta Veterinaria et Zootechnica Sinica,2018,49(7):1387-1393.(in Chinese)
[5]NIOZAS G,TSOUSISS G,STEINHOFEL I,et al.Extended lactation in high-yielding dairy cows.I.Effects on reproductive measurements[J].Journal of Dairy Science,2019,102(1):799-810.
[6]谭益,何东健,郭阳阳,等.基于Storm的奶牛发情实时监测系统设计与实现[J].中国农业科技导报,2018,20(12):83-90.TAN Yi,HE Dongjian,GUO Yangyang,et al.Design and implementation of real-time monitoring system for cow estrus based on Storm[J].Journal of Agricultural Science and Technology,2018,20(12):83-90.(in Chinese)
[7]BURNETT T,MADUREIRA A,SILPER B,et al.Integrating an automated activity monitor into an artificial insemination program and the associated risk factors affecting reproductive performance of dairy cows[J].Journal of Dairy Science,2017,100(6):5005-5018.
[8]赵凯旋,李国强,何东健.基于机器学习的奶牛深度图像身体区域精细分割方法[J].农业机械学报,2017,48(4):173-179.ZHAO Kaixuan,LI Guoqiang,HE Dongjian.Fine segment method of cows'body parts in depth images based on machine learning[J].Transactions of the Chinese Society for Agricultural Mechinery,2017,48(4):173-179.(in Chinese)
[9]AUNGIER S P M,ROCHE J F,SHEEHY M,et al.Effects of management and health on the use of activity monitoring for estrus detection in dairy cows[J].Journal of Dairy Science,2012,95(5):2452-2466.
[10]SMITH D,RAHMAN A,BISHOP-HURLEY G,et al.Behavior classification of cows fitted with motion collars:decomposing multi-class classification into a set of binary problems[J].Computers and Electronics in Agriculture,2016,131:40-50.
[11]DOLECHECK K,SILVIA W,HEERSCHE G,et al.Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies[J].Journal of Dairy Science,2015,98(12):8723-8731.
[12]GAUDE I,KEMPF A,STRUVE K,et al.Comparison of visual and computerized estrous detection and evaluation of influencing factors[J].Animal Reproduction Science,2017,184:211-217.
[13]LEROY C,WALTON J,LEBLANC S.Estrous detection intensity and accuracy and optimal timing of insemination with automated activity monitors for dairy cows[J].Journal of Dairy Science,2018,101(2):1638-1647.
[14]彭琛,韩立新.基于深度强化学习的计步方法[J].计算机与现代化,2019(1):63-68.PENG Chen,HAN Lixin.Deep reinforcement learning for step counting approach[J].Computer and Modernization,2019(1):63-68.(in Chinese)
[15]顾静秋,王志海,高荣华,等.基于融合图像与运动量的奶牛行为识别方法[J].农业机械学报,2017,48(6):145-151.GU Jingqiu,WANG Zhihai,GAO Ronghua,et al.Recognition method of cow behavior based on combination of image and activities[J].Transactions of the Chinese Society for Agricultural Mechinery,2017,48(6):145-151.(in Chinese)
[16]高云,郭继亮,黎煊,等.基于深度学习的群猪图像实例分割方法[J].农业机械学报,2019,50(4):179-187.GAO Yun,GUO Jiliang,LI Xuan,et al.Instance-level segmentation method for group pig images based on deep learning[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(4):179-187.(in Chinese)
[17]侯云涛,蔡晓华,吴泽全,等.奶牛行为特征识别方法的研究与实现---基于支持向量机[J].农机化研究,2018,40(8):36-41.HOU Yuntao,CAI Xiaohua,WU Zequan,et al.Research and implementation of cattle behavior character recognition method[J].Journal of Agricultural Mechanization Research,2018,40(8):36-41.(in Chinese)
[18]强茂山,张东成,江汉臣.基于加速度传感器的建筑工人施工行为识别方法[J].清华大学学报(自然科学版),2017,57(12):1338-1344.QIANG Maoshan,ZHANG Dongcheng,JIANG Hanchen.Recognizing construction worker activities based on accelerometers[J].Journal of Tsinghua University(Science and Technology),2017,57(12):1338-1344.(in Chinese)
[19]SMITH D,DUTTA R,HELLICAR A,et al.Bag of class posteriors,a new multivariate time series classifier applied to animal behaviour identification[J].Expert Systems with Applications,2015,42(7):3774-3784.
[20]王俊,张海洋,赵凯旋,等.基于最优二叉决策树分类模型的奶牛运动行为识别[J].农业工程学报,2018,34(18):202-210.WANG Jun,ZHANG Haiyang,ZHAO Kaixuan,et al.Cow movement behavior classification based on optimal binary decisiontree classification model[J].Transactions of the CSAE,2018,34(18):202-210.(in Chinese)
[21]尹令,刘财兴,洪添胜,等.基于无线传感器网络的奶牛行为特征监测系统设计[J].农业工程学报,2010,26(3):203-208.YIN Ling,LIU Caixing,HONG Tiansheng,et al.Design of system for monitoring dairy cattle's behavioral features based on wireless sensor networks[J].Transactions of the CSAE,2010,26(3):203-208.(in Chinese)
[22]SHAHRIAR M,SMITH D,RAHMAN A,et al.Detecting heat events in dairy cows using accelerometers and unsupervised learning[J].Computers and Electronics in Agriculture,2016,128:20-26.
[23]陈春玲,杨天娇,郭雷,等.支持向量机在舍饲肉牛反刍行为分析中的应用[J].沈阳农业大学学报,2017,48(6):751-756.CHEN Chunling,YANG Tianjiao,GUO Lei,et al.Application of support vector machine in analysis of ruminant behavior of beef cattle[J].Journal of Shenyang Agricultural University,2017,48(6):751-756.(in Chinese)
[24]尹令,洪添胜,刘迎湖,等.基于无线传感器网络支持向量机奶牛行为特征识别[J].传感技术学报,2011,24(3):458-462.YIN Ling,HONG Tiansheng,LIU Yinghu,et al.Cow behavioral features recognition using binary decision tree support vector machines based on wireless sensor network[J].Chinese Journal of Sensors and Actuators,2011,24(3):458-462.(in Chinese)
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