Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning

Accurately predicting postharvest quality is crucial for optimizing storage and reducing losses in table grapes. This study explores the potential of fruit color parameters as non-invasive indicators of postharvest weight loss and textural changes. Using convolutional neural networks (CNNs), we developed predictive models based on colorimetric data, achieving high accuracy (R² > 0.80 for weight loss and R² > 0.97 for texture). Additionally, the effects of storage temperature on grape quality were examined, revealing that colder storage at 3°C significantly reduces weight loss and maintains texture better than storage at 10°C. Among tested cultivars, ‘Shine Muscat’ exhibited lower weight loss and superior textural stability compared to ‘Flame Seedless’. These findings highlight the potential of integrating color-based assessments and machine learning models into postharvest monitoring, offering a practical approach for improving quality control and storage management in the grape industry.