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Cow-YOLO:Automatic cow mounting detection based on non-local CSPDarknet53 and multiscale neck

International Journal of Agricultural and Biological Engineering 2024年第3期17卷 193-202页

作者： De Li Junhao Wang Zhe Zhang Baisheng Dai Kaixuan Zhao weizheng shen Yanling Yin Yang Li College of Electrical Engineering and Information Northeast Agricultural UniversityHarbin 150030China College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471023HenanChina College of Animal Sciences and Technology Northeast Agriculture UniversityHarbin 150030China Key Laboratory of Northeast Smart Agricultural Technology of Ministry of Agriculture and Rural Affairs Northeast Agricultural UniversityHarbin 150030China

Cows mounting behavior is a significant manifestation of estrus in *** timely detection of cows mounting behavior can make cows conceive in time,thereby improving milk production of cows and economic benefits of the *** methods of mounting behavior detection are difficult to achieve precise detection under occlusion and severe scale change environments and meet real-time ***,this study proposed a Cow-YOLO model to detect cows mounting *** meet the needs of real-time performance,YOLOv5s model is used as the baseline *** order to solve the problem of difficult detection of cows mounting behavior in an occluded environment,the CSPDarknet53 of YOLOv5s is replaced with Non-local CSPDarknet53,which enables the network to obtain global information and improves the model’s ability to detect the mounting ***,the neck of YOLOv5s is redesigned to Multiscale Neck,reinforcing the multi-scale feature fusion capability of model to solve difficulty detection under dramatic scale ***,to further increase the detection accuracy,the Coordinate Attention Head is integrated into ***,these improvements form a novel cow mounting detection model called Cow-YOLO and make Cow-YOLO more suitable for cows mounting behavior detection in occluded and drastic scale changes ***-YOLO achieved a precision of 99.7%,a recall of 99.5%,a mean average precision of 99.5%,and a detection speed of 156.3 f/s on the test *** with existing detection methods of cows mounting behavior,Cow-YOLO achieved higher detection accuracy and faster detection speed in an occluded and drastic scale-change ***-YOLO can assist ranch breeders in achieving real-time monitoring of cows estrus,enhancing ranch economic efficiency.

关键词： cows mounting automatic detection Cow-YOLO computer vision CSPDarknet53 multiscale neck

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Real-Time Monitoring Method for Cow Rumination Behav‐ior Based on Edge Computing and Improved MobileNet v3

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智慧农业（中英文） 2024年第4期6卷 29-41页

作者： ZHANG Yu LI Xiangting SUN Yalin XUE Aidi ZHANG Yi JIANG Hailong shen weizheng College of Electrical and Information Northeast Agricultural UniversityHarbin 150030China Harbin Aerospace Stellar Data System Technology Co. LtdHarbin 150030China Harbin Electric Machinery Company Co. Ltd.Harbin 150030China

[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow ***,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring ***‐ever,the application of edge device gives rise to the issue of inadequate real-time *** 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 *** 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 *** 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 ***,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation *** 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 rumin

关键词： cow rumination behavior real-time monitoring edge computing improved MobileNet v3 edge intelligence model Bi-LSTM

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Vision-based measuring method for individual cow feed intake using depth images and a Siamese network

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International Journal of Agricultural and Biological Engineering 2023年第3期16卷 233-239页

作者： Xinjie Wang Baisheng Dai Xiaoli Wei weizheng shen Yonggen Zhang Benhai Xiong College of Electrical Engineering and Information Northeast Agricultural UniversityHarbin 150030China College of Animal Science and Technology Northeast Agricultural UniversityHarbin 150030China State Key Laboratory of Animal Nutrition Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing 100193China

Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture *** achieve an automatic and non-contact measurement of feed intake,this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth *** automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24150 samples.A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected *** experimental results show that the mean absolute error(MAE)and the root mean square error(RMSE)of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively,which outperformed existing *** work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.

关键词： computer vision Siamese network cow feed intake depth image precision livestock farming

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基于时空流特征融合的俯视视角下奶牛跛行自动检测方法

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智慧农业（中英文） 2024年第4期6卷 18-28页

作者：代昕王军号张翼王鑫杰李晏兴戴百生沈维政东北农业大学电气与信息学院黑龙江哈尔滨150030 黑龙江东方学院信息工程学院黑龙江哈尔滨150086

[目的/意义]奶牛跛行检测是规模化奶牛养殖过程中亟待解决的重要问题,现有方法的检测视角主要以侧视为主。然而,侧视视角存在着难以消除的遮挡问题。本研究主要解决侧视视角下存在的遮挡问题。[方法]提出一种基于时空流特征融合的俯视... 详细信息

[目的/意义]奶牛跛行检测是规模化奶牛养殖过程中亟待解决的重要问题,现有方法的检测视角主要以侧视为主。然而,侧视视角存在着难以消除的遮挡问题。本研究主要解决侧视视角下存在的遮挡问题。[方法]提出一种基于时空流特征融合的俯视视角下奶牛跛行检测方法。首先,通过分析深度视频流中跛行奶牛在运动过程中的位姿变化,构建空间流特征图像序列。通过分析跛行奶牛行走时躯体前进和左右摇摆的瞬时速度,利用光流捕获奶牛运动的瞬时速度,构建时间流特征图像序列。将空间流与时间流特征图像组合构建时空流融合特征图像序列。其次,利用卷积块注意力模块(Convolutional Block Attention Module, CBAM)改进PP-TSMv2 (PaddlePaddle-Temporal Shift Module v2)视频动作分类网络,构建奶牛跛行检测模型Cow-TSM (Cow-Temporal Shift Module)。最后,分别在不同输入模态、不同注意力机制、不同视频动作分类网络和现有方法 4个方面对比,进行奶牛跛行实验,以探究所提出方法的优劣性。[结果和讨论]共采集处理了180段奶牛图像序列数据,跛行奶牛与非跛行奶牛视频段数比例为1∶1,所提出模型识别精度达到88.7%,模型大小为22 M,离线推理时间为0.046 s。与主流视频动作分类模型TSM、PP-TSM、PP-TSMv2、SlowFast和TimesFormer模型相比,综合表现最好。同时,以时空流融合特征图像作为输入时,识别精度分别比单时间模态与单空间模态分别提升12%与4.1%,证明本研究中模态融合的有效性。通过与通道注意力(Squeeze-and-Excitation, SE)、卷积核注意力(Selective Kernel, SK)、坐标注意力(Coordinate Attention, CA)与CBAM不同注意力机制进行消融实验,证明利用CBAM注意力机制构建奶牛跛行检测模型效果最佳。最后,与现有跛行检测方法进行对比,所提出的方法同时具有较好的性能和实用性。[结论]本研究能够避免侧视视角下检测跛行奶牛时出现的遮挡问题,对于减少奶牛跛行发生率、提高牧场经济效益具有重要意义,符合牧场规模化建设的需求。

关键词：奶牛跛行检测时空融合视频动作分类深度图像注意力机制 TSM

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畜舍养殖环境智能监控研究现状及展望

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华南农业大学学报 2024年第5期45卷 672-684页

作者：付晓魏晓莉严士超戴百生姜润杰周建钊张翼王鑫杰沈维政东北农业大学电气与信息学院黑龙江哈尔滨150030

家畜疫病的发生及传播与养殖环境密切相关,实现畜舍环境信息的精准感知及智能化调控是提高动物健康水平及生产性能的关键,对我国畜牧产业高质量发展具有重要意义。本文从畜舍养殖环境指标体系、精准感知技术与方法、环境优化调控策略及... 详细信息

家畜疫病的发生及传播与养殖环境密切相关,实现畜舍环境信息的精准感知及智能化调控是提高动物健康水平及生产性能的关键,对我国畜牧产业高质量发展具有重要意义。本文从畜舍养殖环境指标体系、精准感知技术与方法、环境优化调控策略及舒适度评估4个方面出发,介绍了影响家畜健康的环境指标、畜舍养殖环境监控设备、无线数据传输优化技术,阐述了环境数据预测模型及分布规律的研究,并重点分析了畜舍调控装备优化设计、多环境参数调控及环境舒适度评估模型等方面的研究和发展现状,最后总结了当前畜舍环境智能监控应用所存在的问题,并对今后养殖环境智能监控发展方向进行了展望,为我国全面发展智慧畜牧业、提升生产效率提供参考。

关键词：畜舍环境智能监控设备精准感知优化调控舒适度评估

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Assessment of dairy cow feed intake based on BP neural network with polynomial decay learning rate

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Information Processing in Agriculture 2022年第2期9卷 266-275页

作者： weizheng shen Gen Li Xiaoli Wei Qiang Fu Yonggen Zhang Tengyu Qu Congcong Chen Runtao Wang College of Electric and Information Northeast Agricultural UniversityHarbin 150030China College of Animal Science and Technology Northeast Agricultural UniversityHarbin 150030China

To overcome the shortcomings of traditional dairy cow feed intake assessment model andBP neural network, this paper proposes a method of optimizing BP neural network usingpolynomial decay learning rate, taking the cow’s body weight, lying duration, lying times,walking steps, foraging duration and concentrate-roughage ratio as input variables andtaking the actual feed intake is the output variable to establish a dairy cow feed intakeassessment model, and the model is trained and verified by experimental data collectedon site. For the sake of comparative study, feed intake is simultaneously assessed by SVRmodel, KNN logistic regression model, traditional BP neural network model, and multilayerBP neural network model. The results show that the established BP model using the polynomial decay learning rate has the highest assessment accuracy, the MSPE, RMSE, MAE,MAPE and R2 are 0.043 kg2/d and 0.208 kg/d, 0.173 kg/d, 1.37% and 0.94 respectively. Compared with SVR model and KNN mode, the RMSE value reduced by 43.9% and 26.5%, it isalso found that the model designed in this paper has many advantages in comparison withthe BP model and multilayer BP model in terms of precision and generalization. Therefore,this method is ready to be applied for accurately evaluating the dairy cow feed intake, andit can provide theoretical guidance and technical support for the precise-feeding and canalso be of high significance in the improvement of dairy precise-breeding.

关键词： Cow Feed intake assessment BP neural network Polynomial decay learning rate

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Remote monitoring system for livestock environmental information based on LoRa wireless ad hoc network technology

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International Journal of Agricultural and Biological Engineering 2022年第4期15卷 79-89页

作者： Xiao Fu weizheng shen Yanling Yin Yu Zhang Shichao Yan shengli Kou Tengyu Qu Musabimana Jacqueline College of Electrical and Information Northeast Agricultural UniversityHarbin 150030China

The environmental quality of livestock houses is key to livestock breeding and directly affects the growth and health of *** target the characteristics of long application cycles and large coverage areas for environmental monitoring in large-scale livestock breeding,the current study designed a remote monitoring system to provide livestock environmental information based on LoRa wireless ad hoc network *** system consisted of collection terminals,control terminals,LoRa gateways,and Alibaba Elastic Compute *** realized real-time collection,wireless transmission,storage of multi-sensor node data,and remote *** system was not limited by the selected time or region,because data interaction was achieved by accessing cloud servers using GPRS *** could browse and obtain data from computers and a WeChat mini program from any location with network ***,the system used the improved receiver-based auto rate(RBAR)rate-adaptive algorithm in the LoRa wireless communication *** application on a dairy farm,the results showed that the whole system collected 6140 sets of environmental data from four dairy *** packet loss rate was less than 1%within a communication distance of 604 m,and the communication success rate was greater than 99%.The control instructions were real-time and accurate,and the response time was less than 10 s,which met the remote control needs of large *** system provided powerful data and technical support for precision animal production.

关键词： remote monitoring LoRa wireless ad hoc network livestock environment environment information system

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su shen Wang weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Recognition of sick pig cough sounds based on convolutional neural network in field situations

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Information Processing in Agriculture 2021年第3期8卷 369-379页

作者： Yanling Yin Ding Tu weizheng shen Jun Bao College of Electrical and Information Northeast Agricultural UniversityHarbinPR China Tus College of Digit Guangxi University of Science and TechnologyLiuZhouPR China College of Animal Science and Technology Northeast Agricultural UniversityHarbinPR China Key Laboratory of Swine Facilities Engineering Ministry of AgricultureNortheast Agricultural UniversityHarbinPR China

Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of *** pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease early *** to complicated interferences in piggery,recognition of pig cough sound becomes *** a lot of algorithms have been proposed to recognize the pig cough sounds,the recognition accuracy in field sit-uations still needs *** purpose of this research is to provide a highly accu-rate pig cough recognition method for the respiratory disease alarm *** propose a classification algorithm based on the fine-tuned AlexNet model and feature of the *** the advantages of the convolutional neural network in image recognition,the sound signals are converted into spectrogram images for recognition,to enhance the *** compare the proposed algorithm’s performance with the probabilistic neural net-work classifier and some existing *** results reveal that the proposed algorithm significantly outperforms the other algorithms-cough and overall recognition accuracies reach to 96.8%and 95.4%,respectively,with 96.2%F1-score achieved.

关键词： Convolutional neural network Cough recognition Respiratory diseases detection Sound classification

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A new fusion feature based on convolutional neural network for pig cough recognition in field situations

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Information Processing in Agriculture 2021年第4期8卷 573-580页

作者： weizheng shen Ding Tu Yanling Yin Jun Bao College of Electrical and Information Northeast Agricultural UniversityHarbinPR China College of Animal Science and Technology Northeast Agricultural UniversityHarbinPR China Key Laboratory of Swine Facilities Engineering Ministry of AgricultureNortheast Agricultural UniversityHarbinPR China

Pig cough is considered the most common clinical symptom of respiratory ***,establishing an early warning system for respiratory diseases in pigs by monitoring and identifying their cough sounds is *** this paper,we propose a new fusion feature,namely Mel-frequency cepstral coefficient-convolutional neural network(MFCC-CNN),to improve the recognition accuracy of pig *** obtained the MFCC-CNN feature by fusing multiple frames of MFCC with multiple one-layer *** used softmax and linear support vector machine(SVM)classifiers for *** tested the algorithm through field *** results reveal that the performance of classifiers using the MFCC-CNN feature was significantly better than those using the MFCC *** F1-score increased by 10.37%and 5.21%,and the cough accuracy increased by 7.21%and 3.86%for the softmax and SVM classifiers,*** also analyzed the impact of different numbers of fusion frames on the classification *** results reveal that fusing 55 and 45 adjacent frames resulted in the best performance for the softmax and SVM classifiers,*** this research,we can conclude that a system constructed by simple one-layer CNNs and SVM classifiers can demonstrate excellent performance in pig sound recognition.

关键词： Pig cough recognition MFCC SVM CNN Sound classification

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