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Cow lameness is a complex locomotion behaviour, and effective characteristics are the key to identifying early lameness. Machine learning methods were used to quantify the movement of the hooves and head of cows, and they showed the possibility of using the head- and back-hoof linkage features extracted from motion analysis to classify early lameness. Firstly, the key points of the body in the side-view walking videos were tracked by DeepLabCut (DLC). Based on the curves of hoof movement and head swing, the head-hoof linkage pattern was analysed. Second, the curve of cow's back was extracted and the back-hoof linkage feature was built when the hoof touched the ground. At the same time, the movement curves were analysed to extract five other gait characteristics, including asymmetry of stride length, tracking up, asymmetry in the proportion of supporting phase, landing speed, and moving speed. Multiple models were trained on a dataset containing 212 videos, and 10-fold cross-validation was used to verify the performance of the algorithm. The classification performance was improved by optimising the model and algorithm. Overall accuracy and recall were 89.2% and 90.7%, respectively. The scores of the importance of features calculated by the Chi-square test showed that the head- and back-hoof linkage features play an important role in the classification of early lame cows. The hooves and their linkage features with the head and back of cows proposed in this paper can thus effectively detect early lameness in cows. • DeepLabCut was used to track four hooves and head of walking cow. • Head- and back-hoof linkage features was analysed. • Seven lameness indicators were used to build the model. • The recall of classification for early lame cows was 90.7%. [ABSTRACT FROM AUTHOR]

Image processing technology has been used in precision dairy farming to support management decisions. Vision-based animal identification systems can become a potential alternative to RFID. In this paper, a vision system is proposed to extract body images and identify Holstein cows. Side view videos of dairy cattle walking in a straight line were collected. Cow mask was detected using Adaptive SOM method. The largest inscribed rectangle was extracted to locate the cow's body area. A total of 528 videos were collected from 66 cows, and 3 videos were randomly selected for each cow to build template datasets, while the rest of the videos were used as test data. Feature points of the body image were extracted and matched with the template dataset to identify the unknown cow. Four feature extraction methods and two matching methods were investigated and evaluated. The results showed that the highest identification accuracy was 96.72% when the FAST, SIFT and FLANN methods were used for feature extraction, descriptor, and matching, respectively. However, the combination of ORB and BruteForce had better computational efficiency on the basis of high accuracy. Software was implemented and can realise accurate identification of dairy cattle in real-time. • Vision system for dairy cow individual identification is proposed. • Body image of cows was extracted automatically. • An identification model was designed on the basis of ACE-V principle. • Accuracy of one-step match is 95.41% using ORB features. [ABSTRACT FROM AUTHOR]