PMDS-YOLO: A Lightweight Deep Learning Model for Non-Destructive Citrus Fruit Diameter Estimation and Quality Grading

Citrus fruit size is a core quality attribute that determines commercial value and market acceptance. In postharvest processing, rapid, accurate, and non-destructive diameter estimation is an essential prerequisite for automated grading, yet existing computer vision methods are difficult to deploy on factory edge devices. To address this problem, this paper proposes PMDS-YOLO, a lightweight deep learning model for non-destructive citrus fruit diameter estimation and automated quality grading. The model incorporates four key improvements: width factor compression to 0.125 to eliminate channel redundancy; adoption of a MobileOne backbone embedded with the proposed multi-scale channel-spatial residual attention module (MCSR), which reduces parameter count while enhancing multi-scale feature extraction and background discrimination; introduction of dynamic snake convolution (DySnakeConv) to improve the feature extraction module for better detection of irregular fruit contours; and adoption of soft non-maximum suppression (Soft-NMS) to reduce missed detections of densely overlapping targets. Experimental results on a self-built citrus dataset show that PMDS-YOLO achieves only 0.58 M parameters and 86.41% mAP@0.5:0.95, representing a 76.4% reduction in parameter count and a 0.99 percentage point improvement in mAP over the baseline YOLOv13n. In the diameter estimation stage, a quadratic polynomial regression calibration model reduces the MAE to 2.15 mm (R² = 0.85, RMSE = 3.16 mm) and achieves an overall grading accuracy of 57.0% in the grading evaluation, indicating that while the method demonstrates potential in diameter estimation accuracy, further optimization is required for grading performance. The proposed system provides a low-cost, edge-deployable solution for preliminary fruit size assessment and small-scale sorting applications.