Defect Detection of Apples using PatchCore

In recent years, the aging of skilled workers and the shortage of young workers in agriculture have limited harvest quality control, especially in fruit visual inspection. Therefore, with the technological development of computer versions, deep learning is attended for visual inspection. Deep learning-based anomaly detection models have attracted attention and are applied for visual inspection in the industry. However, few studies have performed experiments on crops in agriculture. In this paper, we propose defect detection method for apples as a representative example of fruits. We use PatchCore, which is one of the state-of-the-art anomaly detection methods, while changing feature extractor, which is trained by ImageNet. In experiment 1, we train the model with normal images, inference with the test images, and evaluate the model. According to the experiment1 results, WideResNet50 is the most effective feature extractor. Furthermore, it can be seen that the tip of the branch of the apple is erroneously detected as a defective portion. From experiment 1 result, in experiment 2, input data are preprocessed using YOLOv7 and OpenCV to remove the branch tip portion of an apple, and we evaluated in the same way as in experiment 1. According to the experiment2 results, we achieved 0.902 for AUROC and 0.889 for AUPR, proving the effectiveness of our proposal.f