Automatic detection of multi-type defects on potatoes using multispectral imaging combined with a deep learning model

Automatic detection of potato multi-type defects remains a challenge because of the diversification in defect size and visual similarity among multi-type defects. In this study, an accurate and fast detection method based on a multispectral (MS) image combined with an improved YOLOv3-tiny model was developed to automatically detect and classify multi-type defects on potatoes. A multispectral imaging system (MSI), covering 25 wavebands with a spatial resolution of 409 x 216 pixels, was used to collect MS images of 428 potato samples, which consisted of defect-free potatoes and defective potatoes (including five types of defects, i.e. germination, common scab, bug-eye, dry-rot, and bruise). By introducing the Res2Net modules into the YOLO v3-tiny network, a detection model called multi-type defects detection network (MDDNet) was developed for detecting multi-type defects on potatoes. Three deep learning models, YOLO-v5x, YOLOv3-tiny, and DY2TNet models were compared with the MDDNet model on 128 testing potato MS images. Experimental results showed that the proposed model achieved the highest mean average precision of 90.26% for potato defects among the four models, with about 75 ms detecting time for each MS image. This research demonstrated that the MDDNet model combined with MSI can be useful for the detection of potato multi-type defects.