RESEARCH ON A LIGHTWEIGHT TOMATO RIPENESS DETECTION METHOD BASED ON SFH-YOLOv11

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Automated detection of tomato ripeness is crucial for achieving precise harvesting and enhancing agricultural productivity. However, detecting tomatoes in natural scenes poses challenges such as missed detections and false positives due to significant variations in target scale, frequent occlusions, and complex backgrounds. Additionally, existing detection models face limitations when deployed on mobile devices. To address these issues, this paper proposes SFH-YOLOv11, a lightweight detection model based on an improved YOLOv11n. Building upon YOLOv11n, this model achieves lightweight performance while maintaining high accuracy through three key enhancements: introducing an attention mechanism in the backbone network to strengthen feature selection capabilities, designing lightweight convolutional modules to reduce model complexity, and reconstructing the feature pyramid network in the neck to enhance multi-scale feature fusion. Experimental results demonstrate that SFH-YOLOv11 outperforms other algorithms, achieving mAP50 and mAP50-95 scores of 91.8% and 78.2%, respectively—representing improvements of 1.7% and 1.0% over the original model. While enhancing performance, SFH-YOLOv11 reduces the number of parameters, computational complexity, and model size by 37.2%, 15.9%, and 34.5%, respectively, compared to the original model. This research provides effective technical support for lightweight maturity detection tasks in complex agricultural scenarios.

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