Computer Vision System and Hyperspectral Imaging for Non-Destructive and Contactless Estimation of Maturity Index and Vitamin C in Candonga Strawberry

The rapid, non-destructive and contactless assessment of fruit maturity and nutritional quality is a key challenge for precision harvesting and postharvest management. This study evaluated the potential of a hyperspectral spectroradiometer, and a Computer Vision System (CVS) based on calibrated color images to predict maturity index and vitamin C content in Candonga strawberries harvested at two ripening stages (nearly mature, 50-70% red surface colouration, and mature, 100% red surface colouration) from three farms. Fruits were subjected to non-destructive and contactless measurements and subsequently analyzed through destructive laboratory assays. Mature fruits showed a high respiration rate and maturity index, whereas nearly mature strawberries exhibited high titratable acidity and vitamin C content. Hyperspectral data were preprocessed and modeled using multiple regression approaches. Linear regression achieved the best performance for maturity index prediction and vitamin C content (R² = 0.81 ± 0.03 and R² = 0.69 ± 0.05, respectively). The analysis of the importance of features consistently identified key spectral regions associated with color changes and antioxidant compounds. Furthermore, the CVS, based on calibrated color images and Forest regression, demonstrated suitable predictive capability, with determination coefficients up to R 2 = 0.71 for maturity index and R 2 = 0.63 for vitamin C. The performance of CVS was lower than hyperspectral analysis due to the cumulative spectral nature of color channels, but color analysis is simpler, faster and cheaper and represent an interesting alternative in specific tasks along the supply chain. The experiments proved both hyperspectral and CVS to be effective for the non-destructive estimation of internal quality attributes in strawberries. The results obtained support the use of optical sensing technologies for real-time quality monitoring and improved decision-making along the strawberry supply chain.