Evaluation Method of Potato Storage External Defects Based on Improved U-Net

The detection of potato surface defects is the key to ensuring potato storage quality. This research explores a method for detecting surface flaws in potatoes, which can promptly identify storage defects such as dry rot and the shriveling of potatoes. In order to assure the quality and safety of potatoes in storage, we used a closed keying method to obtain the pixel area of the mask image for a potato's surface. The improved U-Net realizes the segmentation and pixel area measurement of potato surface defects and enhances the feature extraction capability of the network model by adding a convolutional block attention module (CBAM) to the baseline network. Compared with the baseline network, the improved U-Net showed a much better performance with respect to MIoU (mean intersection over union), precision, and F beta, which were improved by 1.99%, 8.27%, and 7.35%, respectively. The effect and efficiency of the segmentation algorithm were also superior compared to other networks. Calculating the fraction of potato surface faults in potato mask images allows for the quantitative detection of potato surface problems. The experimental results show that the absolute accuracy of the quantitative potato evaluation method proposed in this study was greater than 97.55%, allowing it to quantitatively evaluate potato surface defects, provide methodological references for potato detection in the field of deep processing of potatoes, and provide a theoretical basis and technical references for the evaluation of potato surface defects under complex lighting conditions.