Quantification of early bruises on blueberries using hyperspectral reflectance imaging coupled with band ratio and improved multi-threshold coati optimization algorithm method

Abstract

Bruises resulting from mechanical collisions during picking, packing, and transportation lead to a decline in the quality of blueberry fruits. Therefore, performing a quantitative evaluation in the early stages of fruit bruising is essential for achieving the quality classification of blueberries. The primary aim of this study was to precisely quantify internal damage in blueberries by employing a combination of band ratios and an improved multi-threshold Coati Optimization Algorithm (MT-COA) method. Initially, hyperspectral reflectance images (ranging from 950 to 1650 nm) of both sound and bruised blueberries were acquired 30 min after damage. Secondly, to reduce data dimensionality, data processing utilized principal component analysis and band ratio techniques, and a comparative analysis of these two methods was performed. Subsequently, the band ratio images R1112.6/1470.9 were segmented into three classes, encompassing bruised tissue, sound tissue, and background, using global threshold, Otsu method, watershed segmentation algorithm (WSA), MT-COA method, and improved MT-COA method. Finally, the intersection over Union (IoU) metric was employed to assess the segmentation effectiveness. The bruise ratio index (BRI) was calculated to assess the damage rate of the fruit. The results indicate that by utilizing hyperspectral reflectance imaging in conjunction with band ratio and improved MT-COA method, sound and bruised areas can be segmented from the fruit as early as 30 min after damage. The average IoU accuracy is 70.1%. The model shows the potential to quantify early bruises of blueberries during the postharvest period.