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.

翻译：近年来，农业领域见证了人工智能（AI）的显著进展，特别是大规模基础模型的发展。在这些基础模型中，Meta AI研究团队推出的"分割一切模型"（SAM）作为物体分割任务的一项突破性解决方案脱颖而出。尽管SAM已在多种农业应用中展现成功，但其在家禽产业（特别是散养母鸡场景）中的潜力仍相对未经探索。本研究旨在评估SAM在代表性鸡只分割任务（包括部位分割及红外热成像图像使用）中的零样本分割性能，并探索利用SAM作为分割工具的鸡只追踪任务。结果表明，在整只鸡及部位分割任务中，SAM的性能均优于SegFormer和SETR。基于SAM的物体追踪还为肉鸡的行为与运动模式提供了珍贵数据。本研究有助于更深入理解SAM在家禽科学领域的潜力，并为未来鸡只分割与追踪技术的进步奠定基础。