Tracking and Characterizing Spatiotemporal and Three-Dimensional Locomotive Behaviors of Individual Broilers in the Three-Point Gait-Scoring System

SIMPLE SUMMARY: Objective gait scoring can provide critical insight into broiler chicken welfare, including physiological traits related to leg health status. This research aimed to track and characterize spatiotemporal and three-dimensional locomotive behaviors of individual broilers using three gait scores typically used for evaluating bird walking ability in the U.S. broiler industry. Birds were placed on a custom-built platform and manually gait-scored while top-view videos and depth images were simultaneously recorded. A deep-learning-based interface was used to analyze videos and images, and then locomotive behaviors of individual broilers were analyzed from these extracted metrics. Results show that broilers with lower gait scores (less difficulty walking) presented more obvious lateral body oscillation patterns; further, forward (linear)-moving acceleration along the platform was significantly different for broilers across the three gait-score categories. Locomotive-behavior tracking and characterizing methods could be useful tools for automatically and objectively gait-scoring broilers, hence providing great support for precision broiler management. ABSTRACT: Gait scoring is a useful measure for evaluating broiler production efficiency, welfare status, bone quality, and physiology. The research objective was to track and characterize spatiotemporal and three-dimensional locomotive behaviors of individual broilers with known gait scores by jointly using deep-learning algorithms, depth sensing, and image processing. Ross 708 broilers were placed on a platform specifically designed for gait scoring and manually categorized into one of three numerical scores. Normal and depth cameras were installed on the ceiling to capture top-view videos and images. Four birds from each of the three gait-score categories were randomly selected out of 70 total birds scored for video analysis. Bird moving trajectories and 16 locomotive-behavior metrics were extracted and analyzed via the developed deep-learning models. The trained model gained 100% accuracy and 3.62 ± 2.71 mm root-mean-square error for tracking and estimating a key point on the broiler back, indicating precise recognition performance. Broilers with lower gait scores (less difficulty walking) exhibited more obvious lateral body oscillation patterns, moved significantly or numerically faster, and covered more distance in each movement event than those with higher gait scores. In conclusion, the proposed method had acceptable performance for tracking broilers and was found to be a useful tool for characterizing individual broiler gait scores by differentiating between selected spatiotemporal and three-dimensional locomotive behaviors.