Tomato ripeness detection method based on improved YOLOv11 lightweight model

To address the challenges faced in real-world tomato ripeness detection, such as variable lighting conditions, complex backgrounds, and the trade-off between accuracy and the model being effectively lightweight, this study proposes a lightweight YOLOv11-MHS model. The improvements of the proposed model are reflected in three aspects: (1) the C3k2_MSCB module is designed, which integrates a multiscale convolutional block (MSCB) for multiscale feature extraction and fusion, thereby enhancing detection accuracy; (2) the neck of the model is redesigned as a high-level feature screening-fusion pyramid structure, which fuses key features to improve robustness in cluttered environments while reducing model size; and (3) the C2PSA module is enhanced by introducing the spatial and channel synergistic attention mechanism to improve the ability of the model to handle complex scenes. Experimental results on the same data set show that, compared to the baseline model YOLOv11n, YOLOv11-MHS achieves improvements of 1.7% in mAP0.5 and 2.9% in mAP0.5-0.95, while reducing parameters and model size by 35.2% and 32.7%, respectively. These results demonstrate that YOLOv11-MHS achieves both outstanding accuracy and lightweight performance in tomato ripeness detection, providing technical support for agricultural applications.