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.

Isolation and Identification of Pathogenic Bacteria Causing Bovine Subclinical Mastitis in Eastern Hebei Province

In this study, the milk samples of 1 021 cows in eight dairy farms in Eastern Hebei Province were collected and detected with LMT reagent and somatic cell count for subclinical mastitis. Pathogenic bacteria in subclinical mastitis positive milk samples were isolated and identified.The results showed that 60.63%（619/1 021） of the sampled cows were diagnosed with subclinical mastitis, and mixed infections accounted for 88.21%（546/619） of the cases. In addition, 82 strains of 14 species were isolated from the subclinical mastitis positive milk samples, including 36 strains of Staphylococcus（43.90%）, 33 strains of Streptococcus（40.24%）, 8 strains of Enterobacteriaceae（9.76%） and 5 strains of Corynebacterium（6.10%）, respectively. The results proved that Staphylococcus aureus and Streptococcus agalactiae are the main pathogenic bacteria causing bovine subclinical mastitis in Eastern Hebei Province.