Individual Identification of Dairy Cows Based on Deep Feature Extrac-tion and Matching

Individual identification of dairy cows is the prerequisite for automatic analysis and intelligent perception of dairy cows'behavior.At present,individual identification of dairy cows based on deep convolutional neural network had the disadvantages in prolonged training at the additions of new cows samples.Therefore,a cow individual identification framework was proposed based on deep feature extraction and matching,and the individual identification of dairy cows based on this framework could avoid repeated training.Firstly,the trained convolutional neural network model was used as the feature extractor;secondly,the feature extraction was used to extract features and stored the features into the template feature library to complete the enrollment;finally,the identifies of dairy cows were identified.Based on this framework,when new cows joined the herd,enrollment could be completed quickly.In order to evaluate the application performance of this method in closed-set and open-set individual identification of dairy cows,back images of 524 cows were collected,among which the back images of 150 cows were selected as the training data to train feature extractor.The data of the remaining 374 cows were used to generate the template data set and the data to be identified.The experiment results showed that in the closed-set individual identification of dairy cows,the highest identification accuracy of top-1 was 99.73%,the highest identification accuracy from top-2 to top-5 was 100%,and the identification time of a single cow was 0.601 s,this method was verified to be effective.In the open-set individual identification of dairy cows,the recall was 90.38%,and the accuracy was 89.46%.When false accept rate(FAR)=0.05,true accept rate(TAR)=84.07%,this method was verified that the application had certain research value in open-set individual identification of dairy cows,which provided a certain idea for the application of individual identification in the field of intelligent animal husbandry.

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

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