Automatic behavior recognition of group-housed goats using deep learning

• Published in: Computers and electronics in agriculture Vol. 177; p. 105706
• Main Authors: Jiang, Min, Rao, Yuan, Zhang, Jingyao, Shen, Yiming
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2020; Elsevier BV
• Subjects: agriculture; Behavior; Behavior recognition; Centroids; Deep learning; Drinking; Eating; electronics; Frames (data processing); Frames per second; Goats; Group dynamics; group housing; Group-housed goats; Livestock; Recognition; Video; Video sequences; YOLOv4; Deep learning; YOLOv4; Behavior recognition; Video sequences; Group-housed goats
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2020.105706

•Proposing general behavior recognition framework of group-housed goats.•Investigating appropriate detection model of individual goat based on deep learning.•Incorporating spatial-temporal location features of goats and feeding/drinking zones.•Developing the strategy for achieving real-time analysis of goat behavior.•Achieving high recognition accuracies without animal head detection and extra tools. Daily behavior is one important manifestation for health and welfare status of livestock. In traditional behavior recognition methods, it was often mandatory to detect animal heads or depend on extra tools. To overcome such shortcomings, this paper proposed one efficient behavior recognition approach using deep learning to recognize eating, drinking, active and inactive behaviors of group-housed goats from video sequences of top upper-side view. Firstly, the approach of detecting individual goat was designed by means of investigating the characteristics and suitability of several popular deep learning methods. Secondly, we proposed a general behavior recognition framework of group-housed goats for videos acquired from top upper-side view. Four types of goat behaviors were recognized by analyzing the spatial location relationship between goat bounding boxes and feeding/drinking zones, as well as the temporal movement amount of bounding box centroids of the same goat among consecutive frames. One inferential strategy was presented for estimating the missing behaviors caused by goat detection failure in frames. The experimental results showed that YOLOv4 was superior to other models in terms of both goat detection speed and accuracy, and the average recognition accuracies of 97.87%, 98.27%, 96.86% and 96.92%, respectively, for eating, drinking, active and inactive behaviors were achieved on the experimental videos, in real-time manner with the average analysis speed of 17 frames per second on a conventional hardware configuration. Hence, it was demonstrated that the proposed approach could offer one effective way for automatically conducting comprehensive behavior recognition of group-housed livestock.