Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning - Xiaoyan Cheng; Yao Zhou; Zhengyang Huo; Ruiying Li; Shiqian Xu; Hao Qi; Jianyuan Zhu; Fei Wang; Yang Bi (Future Foods)
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.