Attention-Based Multi-View Fusion YOLO for Non-Destructive Pineapple Sweetness Assessment

Classifying the sweetness level of pineapples is an important part of quality control, but existing methods still face issues of subjectivity and require destructive testing. Manual assessment is often inconsistent, while refractometer measurements require cutting the fruit open. Single-view computer vision offers a non-destructive alternative, yet its performance remains limited because visual cues related to sweetness appear on different sides of the fruit. This study introduces Multi-View Fusion YOLO (MVF-YOLO), a model that combines five viewpoints (full, front, left, right, and back) through an attention mechanism to perform adaptive sweetness classification. The dataset consists of 570 pineapples with TSS/TA ratios as the ground truth, producing 2,850 images grouped into three categories: sour (TSS/TA 10–20), ideal (TSS/TA 20–30), and very sweet (TSS/TA >30). MVF-YOLO achieved an mAP@0.5 of 82.1% and an overall accuracy of 84.2%, outperforming the single-view baseline by 14.3%. Attention weight analysis indicates that the full view contributes the most (0.267). With an inference time of 45.8 ms per fruit, the model is sufficiently efficient for use by farmers, distributors, and consumers. The results demonstrate that a multiview approach enhanced with learned attention can significantly improve sweetness classification accuracy without compromising computational efficiency.