In recent years, artificial intelligence (AI) advancements have greatly influenced the agricultural industry, particularly with the emergence of large foundationmodels. One such model, the Segment Anything model (SAM...
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In recent years, artificial intelligence (AI) advancements have greatly influenced the agricultural industry, particularly with the emergence of large foundationmodels. One such model, the Segment Anything model (SAM) developed by Meta AI Research, has revolutionized object segmentation tasks. While SAM has demonstrated success in various agricultural applications, its potential in the poultry industry, specifically regarding cage-free hens, remains largely unexplored. This study aims to evaluate SAM's zero-shot segmentation performance for chicken segmentation tasks, including part-based segmentation and the utilization of infrared thermal images. Additionally, it investigates SAM's ability to predict weight and track chickens. The results highlight SAM's superior performance compared to SegFormer and SETR for both whole and part-based chicken segmentation. SAM demonstrated remarkable performance improvements, achieving a mean intersection of union (mIoU) of 94.8% when using the total points prompts. Furthermore, SAM-based chicken segmentation provides valuable insights into weight prediction, as well as behavior and movement patterns of broiler birds. These findings contribute to the understanding of SAM's potential in poultry science, paving the way for future advancements in chicken segmentation and tracking using large foundationmodel.
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