CF-SAM: An Efficient and Precise SAM Model for Instance Segmentation of Cotton Top Leaves

The complexity of field environments poses significant challenges for the segmentation of cotton top leaves, a critical step for apical bud localization in intelligent topping systems. Conventional segmentation models typically rely on large annotated datasets and high computational costs to achieve high precision and robustness. To address these challenges, this paper proposes an efficient and accurate segmentation model, CF-SAM, built upon the Segment Anything Model (SAM) framework. CF-SAM integrates a lightweight Tiny-ViT encoder to reduce computational overhead and employs a LoRA-based fine-tuning strategy for domain adaptation, achieving improved performance with minimal parameter increments. In addition, an Adaptive Prompting Strategy (APS) is introduced to automatically generate high-quality point prompts, enabling fully automated and end-to-end instance segmentation. Trained on only 1000 field images, CF-SAM achieves 98.0% mask accuracy and an [email protected] of 97.83%, while maintaining real-time inference at 58 FPS with only 0.091 M (0.8%) additional parameters. These results demonstrate that CF-SAM achieves an excellent balance between segmentation accuracy and computational cost, providing a reliable technical foundation for apical bud localization and precision agricultural operations.