Zero-shot image segmentation for monitoring thermal conditions of individual cage-free laying hens

Body temperature is a critical indicator of the health and productivity of egg-laying chickens and other domesticated animals. Recent advancements in thermography allow for precise surface temperature measurement without physical contact with animals, reducing animal stress from human handling. Gold standard temperature analysis via thermography requires manual selection of limited points for an object of interest, which could be time-consuming and inadequate for representing the comprehensive thermal profile of a chicken’s body. The objective of this study was to leverage and optimize a zero-shot artificial intelligence technology for the automatic segmentation of individual cage-free laying hens within thermal images, providing insights into their overall thermal conditions. A zero-shot image segmentation model (Segment Anything, “SAM”) was modified by replacing manual selections of target points with automatic selection of the initial point using pre-processing techniques (e.g., thresholding) in each thermal image. The model was also incorporated with post-processing techniques integrated with a machine learning classifier to improve segmentation accuracy. Three versions of modified SAM models (i.e., SAM, FastSAM, and MobileSAM), two common instance segmentation algorithms (i.e., YOLOv8 and Mask R-CNN), and two foundation segmentation models (i.e., U²-Net and ISNet) were comparatively evaluated to determine the optimal one for bird segmentation from thermal images. A total of 1,917 thermal images were collected from cage-free laying hens (Hy-Line W-36) at 77–80 weeks of age. The image dataset exhibited considerable variations such as feathers, bird movement, body gestures, and the specific conditions of cage-free facilities. The experimental results demonstrate that the modified SAM did not only surpass the six zero-shot models—YOLOv8, Mask R-CNN, FastSAM, MobileSAM, U²Net, and ISNet—but also outperformed other modified SAM-based models (Modified FastSAM and Modified MobileSAM) in terms of hen detection performance, achieving a success rate of 84.4 %, and in segmentation performance, with an intersection over union of 85.5 %, recall of 91.0 %, and an F1 score of 92.3 %. The optimal model, modified SAM, was pipelined to extract statistics including the averages (°C) of mean (27.03, 27.04, 28.53, 26.68), median (26.27, 26.84, 28.28, 26.78), 25th percentile (25.33, 25.61, 27.26, 25.53), and 75th percentile (28.04, 27.95, 29.22, 27.55) of surface body temperature of individual laying hens in thermal images for each week. More statistics of hen body surface temperature can be extracted based on the segmentation results. The developed pipeline is a useful tool for automatically evaluating the thermal conditions of individual birds.