Individual Behavioral Identification and Differential Analysis of Free-range Laying Hens Based on Improved YOLO v8n Model

Abstract

Poultry behavior is closely related to its physiological state, and behavioral data can be used to assess the health status of poultry. Statistical individual behavioral data is needed for laying hen behavioral identification and individual identification, to address the behavioral identification process, laying hen body size was small, aggregation of shade, breeding environment lighting changes and other factors resulting in the laying hen effective features expression was insufficient, individual behavioral identification effect was not ideal problem, based on the YOLO v8n network to build behavioral identification model, while fusing ODConv, GhostBottleneck, GAM attention and Inner-IoU structure, and the model was improved by reducing image feature loss, amplifying global interaction information, fusing crossstage features, and enhancing the feature extraction and generalization ability, which improved the recognition accuracy of five behaviors of laying hens, namely, feeding, drinking, standing, feather arranging, and stooping to search. Meanwhile, the individual identification network was constructed based on the YOLO v8n model, and the individual identification network model was optimized by introducing the MobileNetV3 module, which improved the statistical efficiency of individual behavioral data. The experimental results showed that the optimized behavior identification model achieved 94.4%, 93%, 90.7%, 91.7%, 86.9% average precision (AP) for the recognition of feeding, drinking, standing, feather arranging, and stooping searching behaviors, respectively, and 91.4% mean average precision (mAP), which was comparable to that of YOLO v5n, YOLO v6n, and YOLO v7-tiny, YOLO v8n, the mean average precision mean (mAP) was increased by 4.8, 4.1, 5.5, and 3.5 percentage points, respectively;the number of parameters and the amount of operations of the individual identification model were reduced by 1.9651×106 and 6.1×109 compared with that of the YOLO v8n model.It was found that by analyzing the behavioral data of the laying hens, the behavioral data were related to the temperature and the individual laying hens themselves, and that when the temperature was decreased, the number of feeding and standing was increased, the number of drinking was decreased, the number of finishing feathers and stooping to search almost did not change, the behavioral data of different individual laying hens varied greatly at the same temperature, and the value of the difference was related to the body size of the laying hens. The results of the experiment laid the foundation for judging the health status of laying hens based on behavioral data, precision breeding on farms and preferential selection of individual laying hens.

Keyword: laying hen ; behavior recognition ; YOLO v8n ; multi-target recognition ; MobileNetV3 ; ODConv