Multimodal Fusion of X-Ray Transmission and Dark Field Imaging for Apple Internal Disorders Detection

Jiaqi He, Astrid Tempelaere, Janne Vignero, Pieter Verboven, Bart Nicolai; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 7229-7238

Apples are typically harvested once a year and stored under controlled atmosphere conditions to preserve quality. However, internal defects such as internal browning and cavities can develop during storage. X-ray phase contrast imaging, which simultaneously captures transmission and dark field modalities, has emerged as a promising non-destructive technique for internal quality assessment. These two complementary modalities require effective fusion strategies to be fully leveraged in automated inspection systems. This study investigates three fusion strategies, namely pixel-level, feature-level, and decision-level, combined with multiple feature extraction methods and classifiers for binary classification of healthy and defective apples using X-ray dark field and transmission images. Models were evaluated on two test sets from different seasons, storage conditions and durations. Feature-level fusion using DINO ViT-B/8 feature extractor and logistic regression achieved the highest performance, reaching a balanced accuracy of 0.92 on the within-distribution test set and 0.94 for another independent test set. The findings highlight the importance of combining robust feature representations with multimodal fusion to improve classification performance and generalization. The proposed framework could be extended to other horticultural products exhibiting internal quality disorders.