  In recent years, the agricultural industry has witnessed significant
advancements in artificial intelligence (AI), particularly with the development
of large-scale foundational models. Among these foundation models, the Segment
Anything Model (SAM), introduced by Meta AI Research, stands out as a
groundbreaking solution for object segmentation tasks. While SAM has shown
success in various agricultural applications, its potential in the poultry
industry, specifically in the context of cage-free hens, remains relatively
unexplored. This study aims to assess the zero-shot segmentation performance of
SAM on representative chicken segmentation tasks, including part-based
segmentation and the use of infrared thermal images, and to explore
chicken-tracking tasks by using SAM as a segmentation tool. The results
demonstrate SAM's superior performance compared to SegFormer and SETR in both
whole and part-based chicken segmentation. SAM-based object tracking also
provides valuable data on the behavior and movement patterns of broiler birds.
The findings of this study contribute to a better understanding of SAM's
potential in poultry science and lay the foundation for future advancements in
chicken segmentation and tracking.
