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

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 deve...

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Bibliografiska uppgifter
Huvudupphovsmän: Xiaoyan Cheng, Yao Zhou, Zhengyang Huo, Ruiying Li, Shiqian Xu, Hao Qi, Jianyuan Zhu, Fei Wang, Yang Bi
Materialtyp: Artikel
Språk:engelska
Publicerad: Elsevier 2025-12-01
Serie:Future Foods
Ämnen:
Postharvest deterioration
Non-destructive evaluation
Predictive analytics
Storage stability
Machine learning applications
Länkar:http://www.sciencedirect.com/science/article/pii/S2666833525001613
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http://www.sciencedirect.com/science/article/pii/S2666833525001613

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