LAUSR

Abstract In addition to other surface quality attributes such as size, color and shape, during sorting of harvested apple fruit, early detection of decay is important due to its infectiousness and potential food safety issue. However, automatic and fast inspection of fruit for decay still remains a major problem for the industry. The use of hyperspectral imaging technique makes it possible to perform detection process automatically. Three spectral regions including Vis-NIR (400–1000 nm), Vis (400–780 nm) and NIR (781–1000 nm) were performed using principal component analysis (PCA) to determine the more effective spectral region and PC vector for distinguishing between sound and decayed tissues. Based on the selected PC, loadings corresponding to each wavelength were analyzed to extract key wavelength images in raw hyperspectral data for multispectral image processing. Two sets of multispectral PC score images from Vis-NIR and NIR regions, respectively, were established. To avoid over-segmentation of traditional standard watershed segmentation, global threshold and Ostu, a novel improved watershed segmentation algorithm based on morphological filtering and morphological gradient reconstruction as well as marking constraint were proposed to segment decayed spots on apples. All samples including 220 decayed and 220 sound fruit were used to assess performance of the proposed algorithm. The classification results indicated that 99% of the decayed fruit and 100% of sound fruit were accurately identified by proposed algorithm based on PC3 score images obtained from multispectral PCA of four key wavelengths in NIR region, respectively. This study demonstrated that multispectral images coupled with the improved watershed segmentation algorithm could be a potential approach for detection of early decay on apples. However, further algorithm optimization is still required obtain higher detection accuracy of decayed apples due to zero tolerance for this type of fruit from consumers and processing industries.