Non-destructive internal disorder segmentation in pear fruit by X-ray radiography and AI.

[Display omitted] • Deep learning was able to label defects in X-ray radiographs of pear fruit. • A large amount of ground truth data were obtained by simulations from X-ray CT data. • Synthetic training data improved segmentation of browning and cavity defects. A particular challenge for food quality inspection remains the quantification of internal defects. This work addresses the challenge of internal defects segmentation in pear fruit (cv. Conference) based on high-throughput inline X-ray radiography images in combination with deep learning. Unlike previous approaches, which focused on healthy vs defect classification, the current segmentation approach is able to determine the type of defect, its dimensions, and location. To this end, a novel simulation method was designed to obtain input-target image pairs of radiography data, and more diverse defect pears were generated using a conditioned generator model. The obtained data contributed to the design of a segmentation model that labeled every pixel in the X-ray radiographs of pear fruit as 'external air', 'healthy tissue', 'core', 'browning', or 'cavity'. We demonstrated that additional synthetic data in the learning process drastically improved the model performance. For instance, the mean IoU increased from 0.781 ± 0.112 to 0.883 ± 0.088 for consumable pears with minor cavities and browning. In terms of utility, our segmentation maps provide more detailed information about the type, size, and location of the disorders compared to the heatmaps produced by existing benchmark classifiers. [ABSTRACT FROM AUTHOR]