TMAF-YOLO: A Lightweight Model for In Situ Detection of Tomato Maturity and Defective Fruits in Greenhouses

Accurate tomato maturity detection is essential for harvesting decisions, quality grading, and postharvest handling in greenhouse production. However, leaf occlusion, fruit overlap, complex backgrounds, illumination variation, and subtle color differences between adjacent maturity stages limit real-time detection performance. To improve accuracy and deployment efficiency, this study proposes TMAF-YOLO, a lightweight detection model based on YOLOv8n. In this model, LGhostConv replaces selected convolutional structures to reduce redundant computation. A tomato maturity-aware aggregation fusion module, termed TMAF, is introduced to enhance color, texture, and local structural feature representation in the detection branches. MA-CB Focal Loss is used to improve learning under class imbalance and hard-sample conditions. Experimental results showed that TMAF-YOLO achieved Precision, Recall, mAP50, and mAP50-95 values of 0.889, 0.873, 0.954, and 0.776, respectively. The model contained 2.647 M parameters and required 7.3 GFLOPs, with an inference speed of 209.030 FPS. Compared with YOLOv8n and heavier detection models, TMAF-YOLO achieved higher detection accuracy with fewer parameters and lower computational cost. It also outperformed YOLOv11n and YOLOv12n in detection accuracy while maintaining real-time inference performance. These results indicate that the proposed model is suitable for real-time greenhouse tomato maturity detection and can support automated harvesting and grading.