Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images.

• A dataset of X-ray radiographs of pear fruit was simulated from CT scans. • Deep anomaly detection models were trained on data of healthy pears and tested on data of healthy and defect pears. • Synthetic anomalies in training improved performance over the unsupervised case. • High AUC scores (up to 0.96) and classification accuracy (up to 95%) were obtained. • Defect pears with a cavity percentage > 1.0% could be detected 100% accurate. To preserve the quality of fresh fruit after harvest and to meet the year-round demand for high-quality fruit, pears are stored under a controlled atmosphere. However, due to preharvest events or suboptimal storage conditions, internal disorders might develop resulting in severe quality loss. Examples include internal browning and cavities, which are invisible externally. Here, X-ray radiography is investigated as a technique for internal quality inspection. The detection of defect fruit is approached as an anomaly detection (AD) problem, in which a model is constructed using nominal data and an anomaly score is used to identify defect fruit. In this work, multiple deep AD methods are shown to be effective to detect pears with internal cavity and browning disorders using X-ray radiographs (mean area under the receiver operating characteristic curve (AUC) up to 0.962). The best performing methods were found on par with a state-of-the-art multisensor disorder detection method (mean AUC up to 0.966). By investigating AD performance in function of internal disorder severity, it was shown that defect fruit with a cavity volume percentage > 1.0% could be detected 100% accurate using inline X-ray imaging. For lower cavity area percentages, the accuracy depended on the internal browning severity. Additionally, the explainability of the deep AD methods, i.e., how well human interpretable insight can be provided from each method's predictions, were qualitatively evaluated using anomaly heatmaps, which provided useful insight in the execution of the deep learning algorithms. [ABSTRACT FROM AUTHOR]