A Machine Vision-Based Method for Online Grading and Non-Destructive Weight Measurement of Passion Fruit

Addressing technical challenges such as inaccurate appearance detection, inaccurate weight estimation, and low automation levels in passion fruit sorting under postharvest conveyor-line conditions, this study proposes an intelligent detection and grading model, YOLOv11n-ACH, based on an improved YOLOv11n. By integrating the Hybrid Inverted Block (HIB) module, the ASF-YOLO scale fusion mechanism, and the Convolutional Attention Fusion Mechanism (CAFM), the model effectively mitigates severe fruit occlusion and background interference caused by conveyor surfaces, residual plant material, and illumination variation. Consequently, the high-precision metric mAP@50-95 reaches 99.4%, representing an increase of 3.9 percentage points over the baseline model. Building upon this foundation, a real-time grading and counting system incorporating a confidence-priority frame-selection mechanism was constructed by combining the ByteTrack multi-object tracking algorithm with horizontal dynamic scale calibration technology. The study establishes a multivariate linear regression mass-estimation model based on morphological features (R2 = 0.9617). The regression model was developed using 500 fruits, and its performance was independently evaluated using a second, non-overlapping cohort of 500 fruits collected from the same orchard. Using 12 horizontal calibration points, a cubic spline interpolation function was constructed to compensate for horizontal position-dependent variation in the pixel-to-physical scale under the tested fixed imaging configuration. In the independent mass-validation cohort, the system achieved an MAE of 2.54 g, an RMSE of 3.21 g, and an MARE of 5.65%. A third, non-overlapping cohort of 1568 fruits was used for end-to-end passage-level counting and operational grading evaluation. This lightweight solution provides an engineering approach for passion-fruit sorting under the tested postharvest conveyor-line conditions.