A Multi-scale Differentiated Feature Fusion Network for Real-time Tomato Maturity Detection

To address low accuracy and poor robustness in tomato maturity detection under complex agricultural environments (light variations, occlusion, multi-scale targets), this study proposes a lightweight multi-scale differential fusion network Tomato Net-MSF. Its core innovation is scale-customized feature enhancement modules: (1) Small-scale KSFA module (multi-core selection + spatial-spectral attention) suppresses background and improves small-target recognition; (2) Medium-scale C3k2l module (2*2 simplified convolution + dual-bottleneck stacking) enhances fusion stability; (3) Large-scale LSConv module (global perception + dynamic aggregation) refines target contours. Experiments on Laboro Tomato dataset show: mAP50 0.75 (8% higher than baseline YOLOv11), F1 0.72 (6% higher), and 50FPS inference speed (20ms/frame) on RK3588NPU (5-fold acceleration). The model optimizes multi-scale detection balance, integrating high precision, robustness and edge deployment capability, providing an efficient solution for real-time tomato maturity detection in complex scenarios.