A deep learning method based on YOLOv5 and SuperPoint-SuperGlue for digestive disease warning and cage location backtracking in stacked cage laying hen systems

Digestive diseases are common poultry diseases that significantly affect production and animal welfare. Early warning of digestive disease outbreaks is essential in poultry breeding. In conventional cage laying hen systems, breeders usually spend 1–2 days randomly selecting hens for health assessment. This process is time-consuming, labor-intensive, and may cause stress to the hens, which goes against animal welfare principles. In this paper, a model structure was proposed to locate the cage of sick hens based on manure images using digital image processing and deep learning. Abnormal features of poultry manure was extracted using the YOLOv5 and combined with the motion information of the manure belt estimated by the SuperPoint-SuperGlue model to construct an early warning and traceability system for diseases in a layered cage farming mode. This system enables the automatic detection and traceability of digestive diseases in layer-raised hens. The results showed that YOLOv5 can effectively recognize abnormal chicken manure with precision rate, recall rate, mAP@0.5 and mAP@[0.5:0.95] of 95.7 %, 95.4 %, 98.1 % and 84.3 %. The SuperPoint-SuperGlue model demonstrated strong data fitting capabilities and remarkable robustness in long-distance monitoring of manure belt movement, with MAE, RMSE, and MAPE values of 0.19 m, 0.14 m, and 0.34 %, respectively. After combining the SuperPoint-SuperGlue with a self-designed error correction module, the traceability accuracy of the model was validated through 48 field tests, achieving a correct tracing rate of 91.6 %. These research results can be used for providing early warnings of digestive system diseases in caged chickens, providing a prerequisite for the development of intelligent disease diagnostic equipment.