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Toward trustworthy non-destructive mango quality prediction using hyperspectral imaging and explainable AI | Synapse
March 3, 2026
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Toward trustworthy non-destructive mango quality prediction using hyperspectral imaging and explainable AI
HD
Haizhen Ding
Nanjing Agricultural University
JZ
Jingyuan Zhao
Nanjing Agricultural University
WW
Wensi Wang
Nanjing Agricultural University
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Key Points
Quality prediction is achieved through advanced hyperspectral imaging techniques, enhancing accuracy and trust.
The method focuses on fruit ripeness assessment, essential for consumer satisfaction and market value.
Utilization of explainable AI ensures that predictions are transparent and understandable for stakeholders.
This approach may enable significant improvements in agricultural practices and food supply chain management.
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Ding et al. (Mon,) studied this question.
synapsesocial.com/papers/69a76639badf0bb9e87dc354
https://doi.org/https://doi.org/10.1016/j.foodcont.2026.112026
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