Food quality standards are evolving to meet consumer demands and sustainability goals. Quality controls are essential throughout the supply chain, but manual assessment is subjective, time-consuming, and costly. Computer vision systems (CVS) offer a potential solution by integrating cameras and computers to automate the evaluation and sorting process. The study contributes to explore the use of computer vision on a new market segment, the one of red-flesh kiwifruit, with the purpose of ensuring consistent quality attributes for consumers while supporting the supply chain. For red kiwifruit, accurately assessing flesh redness poses a challenge, which was addressed in this study through the implementation of a robust CVS that i) exploits an artificial neural network to detect human perceived and commercially determined red shades in kiwifruit images, ii) computes image descriptors, iii) and grades the fruit with the use of unsupervised learning algorithm. Cohen’s K-score analysis showed that machines have higher and more consistent agreement (k = 0.60) with the reference evaluation made by experienced fruit quality graders. Human inspectors demonstrated to produce evaluations affected by perception subjectivity and high variance throughout the day (k between 0.38-0.54). The analysis of Pearson correlation highlighted that the CVS shows a Pearson correlation in range 0.87-0.91 when compared to human evaluations.