SAM for Poultry Science
Xiao Yang Haixing Dai Zihao Wu Ramesh Bist Sachin Subedi Jin Sun Guoyu Lu Changying Li Tianming Liu Lilong Chai
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.