Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning

Highlights：

• Healthy and defect Conference pears resulted from storage experiments.

• X-ray CT scans were taken and internal disorders were manually annotated.

• A deep neural network was trained to segment internal disorders automatically.

• Quantitative data of the internal quality was used to accurately sort the fruit.

• The method can be used for non-destructive internal food quality inspection.