Chicken body temperature monitoring method in complex environment based on multi-source image fusion and deep learning

Abstract

Severe diseases in chickens present substantial risks to poultry husbandry industry. Notably, alterations in body temperature serve as critical clinical indicators of these diseases. Consequently, timely and accurate monitoring of body temperature is essential for the early detection of severe health issues in chickens. This study presents a novel method for simultaneous body temperature detection of multiple chickens in caged poultry environments. A dataset of 2896 chicken head images was developed. The YOLOv8n-mvc model was created to accurately detect chicken head positions and extracted temperature data and distance information through the fusion of RGB, thermal infrared, and depth images. The chicken head temperature was calibrated using distance information. The YOLOv8n-mvc model established in this study achieved a precision of 91.6 %, recall of 92.5 %, F1 score of 92.0 %, and [email protected] of 96.0 %. The model was successfully deployed on an edge computing device for validation tests, demonstrating its feasibility for chicken body temperature detection. This study provides a reference for developing a chicken health monitoring system based on body temperature.