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

Long term storage is required to deliver high quality pear fruit year-round. Under suboptimal storage conditions, internal disorders, such as internal browning and cavity formation, can develop and are often invisible from the outside. We present a non-destructive inspection method to quantify internal disorders in X-ray CT scans of pear fruit using a deep neural network for semantic segmentation. Herein, a U-net based model was trained to automatically indicate healthy tissue, core and regions affected by internal disorders, i.e., cavity formation and internal browning. The quantitative data resulting from the segmentations was used to measure the severity of internal disorders. Excellent classification accuracies of 99.4 and 92.2% were obtained for the classification of ?consumable? vs ?non-consumable? fruit on the one hand and ?healthy? vs ?defect but consumable? vs ?nonconsumable? fruit on the other hand. The identification of ?defect but consumable? fruit showed to be the most difficult.