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...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。展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed fede展开更多
文摘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。
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed fede
