Red-flesh kiwifruit inner quality scoring with a computer vision system

Red-fleshed kiwifruits are a recent entry to the global market, and their nutraceutical properties have garnered significant consumer interest. This produces investment opportunities for industries, which must accurately assess the internal quality of these fruit, particularly when faced with long shipping distances and high-quality standards. Evaluating the redness of kiwifruit poses a complex challenge due to the inherent variability in colour localization, as well as the wide range of red shades and intensities within each fruit. The current method employed to assess the colour quality of red-fleshed kiwifruit relies on visual inspections performed by experienced operators. However, this method suffers from complexity, subjectivity, limited repeatability, and a slow evaluation process. In this study, a computer vision system that exploits an unsupervised learning algorithm was developed to score fruit according both to red quantity and red quality. RGB images of sliced fruit were segmented into the hue-saturation-value colour space to generate the RSmask, which was used to extract descriptors for the “red quantity” classifier. Simultaneously, the RSmask was applied to produce a red-related image. The “red quality” score was determined with a K-means classifier that assessed the descriptors derived from the conversion of the red-related image into the CIELAB colour space. Consequently, 102 sample fruit were classified into 36 categories based on the combination of the red quantity and quality scores. The results demonstrated that red colour quantity is much more predictable than colour quality due to human eye colour perception. © 2024 International Society for Horticultural Science. All rights reserved.