Pose estimation and behavior classification of broiler chickens based on deep neural networks

Poultry behavior is an important indicator for diagnosing poultry diseases. Accurate pose estimation is the basis of poultry behavior analysis, and it provides poultry disease warning methods. On large-scale poultry farms, it is usually a farmer or veterinarian who watches the pose of the broiler chicken to determine whether they are sick. When the posture of the bird is abnormal, the breeders can address the problem promptly. Accurate tracking of birds can better estimate their posture. In this paper, pose estimation based on a deep neural network (DNN) is applied to analyze the broiler chicken's behavior for the first time. First, the pose skeleton is constructed through the feature points of the broiler chicken, and then, it is used to track specific body parts. Furthermore, the naive bayesian model (NBM) was used to classify and identify the poses of broiler chickens. Preliminary tests revealed that we could identify chickens in standing, walking, running, eating, resting, and preening states by comparing the postures of classified broiler chickens. The test precision of behavior recognition is 0.7511 (standing), 0.5135 (walking), 0.6270 (running), 0.9361 (eating), 0.9623 (resting), and 0.9258 (preening). Our research provides a noninvasive method for broiler chicken behavior analysis, which can be used for future behavior analysis in broiler chicken farming.

Automated summary

This study utilizes deep neural networks for broiler chicken behavior analysis, constructing pose skeletons and using naive bayesian models to identify behavior, which provides a new method for disease diagnosis and warning in broiler chicken farming.