Enhancement of apple defect identification with semantic segmentation and label-efficient data generation

The increasing demand for automated fruit-sorting systems has driven the development of machine vision and deep learning technologies for postharvest grading of fruit defects. Effective sorting requires identifying not only the presence of defects but also their types and severity to avoid unnecessary rejection of marketable fruits with minor defects. This study applied deep learning-based semantic segmentation models to Fuji apple images, focusing on four defect types: cracks, bruises, diseases, and scars. Model performance was evaluated for both defect classification and severity estimation. To further improve performance, a label-efficient approach using generative adversarial networks was proposed to generate synthetic apple images and defect masks, reducing the need for extensive manual labeling to create a larger dataset. Qualitative and quantitative analyses of the generated results showed that the synthetic dataset successfully mimicked the biological characteristics of apples as well as the shape, position, and size of the defects. The segmentation model's ability to identify defects was enhanced by the proposed synthetic dataset. The R² values for defect severity estimation increased to 0.82, 0.85, 0.75, and 0.92 for cracks, bruises, diseases, and scars respectively, while F1-scores for defect classification reached 100, 94.3, 94.1, and 89.7 %. Furthermore, per-sample classification performance was enhanced with a binary F1-score of 95.9 % for defect presence and a multi-label accuracy of 93.9 % for defect types. This study clearly demonstrates that synthetic datasets generated using generative adversarial networks can substantially enhance both defect type classification and severity estimation in semantic segmentation-based apple defect identification models.