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Abstract Accurate tomato instance segmentation in greenhouse scenes remains challenging because of fruit occlusion, instance contact, ambiguous boundaries, and the visual similarity between green fruits and leaves. This study presents a lightweight YOLO11n-seg framework that combines a morphology-based shape-boundary-driven (SBD) weighting strategy with the previously proposed CMPRD multi-scale context module.
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