摘要
奶牛发情和爬跨行为之间存在着密切的联系,及时发现奶牛的爬跨行为是检测奶牛发情、提高养殖收益需要考虑的重要问题。为了在自然环境下可靠地检测奶牛的爬跨行为,同时避免引起应激反应,研究并提出基于Wi-Fi信号的奶牛爬跨行为检测与识别方法。首先,应用部署在日常生活环境中通用的Wi-Fi设备捕获奶牛的运动状态数据;其次,通过载波聚集、移动加权平均滤波对数据进行预处理;第三,基于局部离群因子LOF算法,实现信号跳变检测并以此为基础获取包含奶牛动作的信道状态信息(Channel State Information,CSI)序列片段;第四,设计并提取CSI序列片段的特征,构建了包含3类奶牛动作,共计8127个样本的数据集;最后,基于长短时记忆网络(Long Short Term Memory,LSTM)构建奶牛行为识别模型。通过使用数据集中2497个样本作为测试集检验提出的网络模型,检验结果表明,系统能够可靠地捕获包含奶牛动作的CSI序列片段,并以较高的准确率识别奶牛的爬跨行为。模型在测试集上对3类样本的总体分类准确率为96.67%,其Kappa系数为0.9431,获得了较高的性能。研究结果将基于Wi-Fi信号的无线感知技术引入农业信息化领域,扩展了动物行为监控的技术手段,为无线传感技术在农业智能化方面的应用提供参考。
In the dairy farming industry,there is a close relationship between estrus and the crawling behavior of dairy cows.Timely detection of the crawling behavior of dairy cows is an important issue to be considered to detect the estrus of cows and improve breeding income.Due to the traditional wearable sensing method is easy to cause animals’stress response and generally detrimental to their welfare,it is necessary to find a new way.In 802.11 a/g/n standards,channel response can be partially extracted from off-the-shelf Orthogonal Frequency Division Multiplexing(OFDM)receivers in the format of the Channel State Information(CSI),which reveals a set of channel measurements depicting the environment changes.To reliably detect and effectively recognize the crawling behavior of dairy cows and avoid stress response in a natural farming environment,a method based on the CSI of Wi-Fi signals was proposed in this study.Firstly,in the breeding shed of about 150 m2,a wireless router was used as the signal transmitter,and a computer equipped with Intel 5300 wireless Network Interface Card(NIC)was used as the signal receiver to set up a Multiple Input and Multiple Output(MIMO)wireless communication system,which could be used to obtain dairy cows’motion state data in the format of the CSI.Secondly,the obtained CSI series data was preprocessed step by step(i)the CSI values of 30 subcarriers in each radio beam were aggregated into one by using the algorithm of carrier aggregation so that the module of signal jump detection could be run;(ii)the environmental noise caused by factors such as temperature and shed layout were filtered by using the algorithm of moving weighted average filtering;(iii)based on the algorithm of local outlier factor,a signal jump detection module was designed to find out the beginning and end time of the dairy cows’motion in each CSI sequence fragment.Thirdly,the characteristics of CSI sequences were designed and extracted to construct a dataset containing 8127 samples of three types of cows’movements.
作者
郝玉胜
林强
王维兰
郭敏
逯玉兰
Hao Yusheng;Lin Qiang;Wang Weilan;Guo Min;Lu Yulan(School of Mathematics and Computer Science,Northwest Minzu University,Lanzhou 730030,China;National Languages Information Technology,Northwest Minzu University,Lanzhou 730030,China;College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2020年第19期168-176,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
西北民族大学中央高校基本科研业务费项目(31920170149)
西北民族大学甘肃省一流学科引导专项资金(11080305)
国家民委创新团队计划资助(〔2018〕98号)
国家自然科学基金项目“面向多流行学习的谱聚类方法及其在运动分割中的应用研究”(61866033)
国家自然科学基金项目“基于健康流数据的健康演进趋势识别与实时状态评测关键技术研究”(61562075)
甘肃省高等学校创新基金项目(2020B-069)。
关键词
畜牧业
奶牛
算法
爬跨行为
Wi-Fi无线感知
信道状态信息
livestock production
dairy cow
algorithms
crawling behavior
Wi-Fi wireless sensing
channel state information
