Non-destructive estimation models often require cultivar-specific calibrations due to spectral differences arising from genetic variability. Integrating diverse cultivars into a single model can reduce costs and simplify data collection. However, in persimmons, the abundant and variable proanthocyanidins in the fruit overlap with spectral regions used for sugar estimation, rendering accurate prediction with a single model challenging. In this study, we attempted sugar estimation in diverse persimmon cultivars using near-infrared (NIR) spectroscopy and hyperspectral imaging. A total of 989 spectral measurements were acquired from 34 persimmon cultivars. Regression models employing various pre-processing and modeling techniques achieved a maximum R² of 0.786, indicating the feasibility of modeling sugar content across diverse cultivars with a unified approach. Furthermore, by designing a cover for the NIR sensor and combining it with SNV pre-processing, we demonstrated that stable spectra for sugar estimation can be obtained under outdoor conditions. With further improvements in accuracy, this approach is expected to facilitate rapid fruit quality evaluation and contribute to optimized production.
