Detection of skin defects on mangoes based on hyperspectral imaging combined with band ratio and improved Otsu method

Abstract

Skin defects are one of the major concerns in post-harvest grading and processing of mangoes. Skin defects can cause mangoes to be easily damaged during transportation and storage, and also raise the risk of infection for other mangoes in the batch. To solve the problems of inefficiency and time-consuming of traditional detection methods of skin defects on mangoes, the hyperspectral imaging combined with band ratio and improved Otsu method for detecting on mangoes was proposed in this study. Firstly, Principal Component Analysis was used to the three regions of Vis-NIR (450–1000 nm), Vis (450–780 nm), and NIR (780–1000 nm). It was found that the PC2 image of the Vis-NIR region and the PC1 image of the Vis region were more effective in distinguishing defect areas. Next, based on the weight coefficient curves derived from the selected PC images, four feature wavelengths (642 nm, 702 nm, 744 nm and 942 nm) were determined. Additionally, the PC images and band ratio images were obtained from these feature wavelength images. The band ratio Q744/942 image was determined to be the most suitable for detecting skin defects on mangoes. Finally, the segmentation results of global thresholding and the improved Otsu method were compared, and it was found that the improved Otsu method had better segmentation results. Mis-segmentation due to the stem-end region and the defective region had the same gray scale features. The band ratio Q744/702 image based on the linear stretch transformation could be used to effectively distinguish the stem-end region and the defect region. The final overall accuracy was 96.67 % with no false positives. The results indicate that the proposed detection method in this study has the potential for online detection of skin defects on mangoes.