Unsupervised anomaly detection for pome fruit quality inspection using X-ray radiography

A novel fully convolutional autoencoder (convAE) was introduced to analyze X-ray radiography images of ‘Braeburn’ apples and ‘Conference’ pears with and without disorders for online sorting purposes. The model was solely trained on either apple or pear samples without disorders and outperformed a traditional autoencoder (AE) across multiple test sets. We evaluated our approach using the area under the curve (AUC) as an evaluation metric. A cross-test experiment further demonstrated consistent performance between a model trained on apple data for classifying pear fruit (accuracy: 71 %) and a pear-specific model (accuracy: 70 %). We also evaluated models trained on simulated X-ray radiographs with real ones, and vice versa. For instance, under scenario of training on real data and testing on simulated X-ray radiographs, an accuracy of 80 % for detecting disordered non-consumable pear was achieved. This work provides valuable insights into anomaly detection for apples and pears with several disorders.