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Dempster Shafer distance-based multi-classifier fusion method for pig cough recognition

International Journal of Agricultural and Biological Engineering 2024年第4期17卷 245-254页

作者： weizheng shen Xipeng Wang Yanling Yin Nan Ji Baisheng Dai shengli Kou Chen Liang School of Electrical Engineering and Information Northeast Agricultural UniversityHarbin 150030China Heilongjiang Agricultural Technology Extension Station Harbin 150036China

High precision pig cough recognition and low computational cost is of great importance for the realization of early warning of pig respiratory *** researchers have improved the recognition rate of pig cough sounds to a certain extent from feature selection and feature fusion ***,there is still a margin for the improvement in the accuracy and complexity of existing ***,it is challenging to further enhance the precision of a single ***,this study proposed a multi-classifier fusion strategy based on Dempster Shafer distance(DS-distance)algorithm to increase the classification *** the engineering implementation,the machine learning with low computational complexity for fusion was ***,three metrics of accuracy and diversity between classifiers were defined,including overall accuracy(OA),double fault(DF),and overall accuracy and double fault(OADF),for selecting the base ***,a two-step base classifier selection approach based on these metrics was proposed to make an optimized selection of features and ***,the proposed DS-distance algorithm was used to fuse the selected base classifiers to create a *** sound data collected in the pig barn verified the proposed *** experimental results revealed that the overall recognition accuracy of the proposed method could reach 98.76%,which was better than the existing *** study has achieved a high recognition accuracy through ensembled machine learning with low computational *** proposed method provided an efficient way for the quick establishment of high precision pig cough recognition model in practice.

关键词： pig cough recognition classifier fusion classifier selection Dempster Shafer fusion distance fusion

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

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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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Rapid Detection of Somatic Cell Count Based on Hybrid Variable Selection Method

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Journal of Northeast Agricultural University(English Edition) 2024年第3期31卷 59-73页

作者： shen weizheng Cui Xiang Wang Yan Nie Debao Zhang Qinggang Zheng Wei Sun Jian Yang Xin Dai Baisheng College of Electrical and Information Northeast Agricultural UniversityHarbin 150030China Heilongjiang Animal Husbandry Service Harbin 150060China School of Information Engineering Shandong Huayu University of TechnologyDezhou 253000ShandongChina

Somatic cell count detection is the daily work of dairy farms to monitor the health of *** feasibility of applying near-infrared spectroscopy to somatic cell count detection was researched in this *** samples with different somatic cell counts were collected and preprocessing methods were *** selection algorithm based on hybrid strategy and modelling method based on ensemble learning were explored for somatic cell count *** model was used to diagnose subclinical mastitis and the results showed that near-infrared spectroscopy could be a tool to realize rapid detection of somatic cell count in milk.

关键词： near-infrared spectroscopy somatic cell count mastitis rapid detection

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Real-Time Monitoring Method for Cow Rumination Behavior 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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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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Rumination recognition method of dairy cows based on the change of noseband pressure

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Information Processing in Agriculture 2020年第4期7卷 479-490页

作者： weizheng shen Aijing Zhang Yu Zhang Xiaoli Wei Jian Sun College of Electricity and Information Northeast Agricultural UniversityHarbin 150030China

Rumination is closely related to ruminant’s health and welfare,so it is very important to monitor *** monitoring equipment based on chewing and swallowing audio is easy to be affected by ambient noise,equipment based on acceleration sensor is not easy to monitor number of cuds and other information,equipment based on video monitoring has high cost and is easy to be affected by weather and other cows,while equipment based on chewing pressure can accurately record the chewing pressure of dairy cows,and use related processing technology to obtain the number of cuds and other *** the related research,the peak value and interval of masticatory pressure peak in time domain were used to identify *** view of the problem that the standard of pressure peak was not well defined due to the different head size and masticatory strength of different age and different breed cows,this paper proposed a method to identify the number of rumination,duration of rumination and number of cuds of cows by using standard deviation and noseband pressure signal frequency *** accurate and stable frequency spectrum range of rumination noseband pressure signal was obtained by frequency spectrum analysis,and the interference of feeding noseband pressure signal to rumination noseband pressure signal was eliminated by using standard *** with the direct observation,the results showed that the recognition accuracy of the method proposed in this paper were 100%,94.2%and 94.45%for the number of rumination,duration of rumination and number of cuds respectively,and the average absolute errors were 0,2.0029%and 2.5623%respectively,which showed that the method can accurately identify the rumination information of dairy cows.

关键词： Rumination Noseband pressure Standard deviation Characteristic frequency

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Prediction of the diet nutrients digestibility of dairy cows using Gaussian process regression

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Information Processing in Agriculture 2019年第3期6卷 396-406页

作者： Qiang Fu weizheng shen Xiaoli Wei Ping Zheng Hangshu Xin Chunjiang Zhao College of Electrical and Information Northeast Agricultural UniversityHarbin 150030PR China Key Laboratory of Pig-breeding Facilities Engineering Ministry of AgricultureHarbin 150030PR China College of Animal Science and Technology Northeast Agricultural UniversityHarbin 150030PR China National Engineering Research Center for Information Technology in Agriculture Beijing 100097PR China

In order to effectively evaluate the diet nutritional value of dairy cows,it is essential to accurately predict the diet nutrients digestibility(DND).Conventional predicting DND methods are usually based on the least squares linear regression analysis(LS-LRA),which often relies on a large amount of training samples to accomplish reliable ***,in real-world applications,it is often extremely difficult,costly and time-consuming to obtain a large number of measured samples,especially for the DND prediction of dairy *** paper applies a Gaussian process regression(GPR)technique to predict the DND indicators of dairy cows in small *** evaluate prediction accuracy effectively,we compared the GPR technique with the LS-LRA,radial basis function artificial neural network(RBF-ANN),support vector regression(SVR)and least squares support vector regression(LS-SVR)methods,using the required sample data obtained from actual digestion *** prediction results indicate that the GPR technique is superior to other conventional methods(especially the LS-LRA method)in predicting the main DND indicators of dairy cows such as dry matter digestibility(DMD),organic matter digestibility(OMD),neutral detergent fiber(NDFD),acid detergent fiber(ADFD),and crude protein digestibility(CPD).It is worth mentioning that the developed GPR-based prediction technique is more suitable for the prediction problems with small samples,which is often the case in the prediction of DND indicators of dairy cows,and then more coincide with actual needs.

关键词： Precision feeding Dairy cow diet evaluation Diet nutrients digestibility Small sample prediction GPR algorithm

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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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Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration

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Information Processing in Agriculture 2020年第3期7卷 427-443页

作者： weizheng shen Fei Cheng Yu Zhang Xiaoli Wei Qiang Fu Yonggen Zhang College of Electrical and Information Northeast Agricultural UniversityHarbin 150030China College of Animal Science and Technology Northeast Agricultural UniversityHarbin 150030China

Ingestive-related behaviors including feeding and ruminating are important indexes to measure the health and welfare of dairy *** purpose of this study is to develop a method based on triaxial acceleration to automatically recognize feeding and ruminating of dairy *** the experiment,five diary cows raised in a barn were used as experimental subjects.A triaxial acceleration sensor was used as the device to collect jawmovement data of dairy cows,and the behaviors of dairy cows were classified into three categories:feeding,ruminating and other *** features of time-domain and frequency-domain were extracted from the raw acceleration *** machine learning algorithms including k-nearest neighbor,support vector machine and probabilistic neural network were used for the classification and the results based on four different data segment lengths were *** results show that the three algorithms can be used for recognition of feeding and ruminating with high *** the condition that the sampling frequency of the sensor is 5 Hz,the combination of data segment length of 256 and k-nearest neighbor algorithm is the best scheme for recognition of feeding and ruminating in this *** precision and recall of recognition for feeding were 92.8%and 95.6%respectively,and those of recognition for ruminating were 93.7%and 94.3%*** specificity and AUC of recognition for feeding were 96.1%and 0.959 respectively,and those of recognition for ruminating were 97.5%and 0.959 *** will provide an effective method for real-time monitoring of ingestive-related behaviors of dairy cows and lay a foundation for prediction of dairy cows’health status and welfare to further achieve the purpose of disease prediction and adjusting feeding and management methods.

关键词： Recognition of ingestive-related behaviors Triaxial acceleration Machine learning

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