A Patch-Aligned Multimodal Deep Learning Framework for Non-Destructive Freshness Monitoring of Postharvest Mushrooms Using Hyperspectral Imaging
Guo, Z., Wang, Y., Cao, L., Auat Cheein, F. and Guo, X. (2026) A Patch-Aligned Multimodal Deep Learning Framework for Non-Destructive Freshness Monitoring of Postharvest Mushrooms Using Hyperspectral Imaging. Foods, 15 (18). ISSN 2304-8158
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F Auat Cheein A Patch-Aligned Multimodal Deep Learning Framework OCR UPLOAD.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
Button mushrooms (Agaricus bisporus) are highly perishable, making rapid and automated postharvest freshness evaluation crucial for cold-chain logistics. Visible-near-infrared (Vis-NIR) hyperspectral imaging is promising for non-destructive food quality assessment, yet mining highly redundant spatial–spectral data remains challenging. This study proposes a novel artificial intelligence approach, the patch-aligned multimodal interaction fusion network (PAMIF-Net), to monitor postharvest mushroom freshness. Using a Vis-NIR dataset of 400 A. bisporus caps over a 9-day refrigerated storage period, this deep learning architecture dynamically fuses global spectral features (indicating internal physicochemical shifts) with localized spatial morphological features (capturing surface deterioration) using a gated attention mechanism. Extensive evaluations across 10 independent trials demonstrated that PAMIF-Net achieved optimal classification accuracy (up to 100% on the current test set) and a minimal mean absolute error for five-class storage time recognition. Furthermore, it exhibited superior computational efficiency and significantly lower inference latency compared to classical machine learning and standard deep learning backbones. This multimodal spatial–spectral deep learning framework demonstrates the feasibility of combining HSI and deep learning for the specific task of automated storage time recognition of A. bisporus under controlled refrigerated conditions.
| Item Type: | Article |
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| Keywords: | multimodal deep learning, non-destructive testing, Vis-NIR hyperspectral imaging, food quality assessment, cold-chain logistics, Agaricus bisporus |
| Divisions: | Departments > Engineering Research Centres > Harper Institute of Technology |
| Depositing User: | Miss Anna Cope |
| Date Deposited: | 16 Sep 2026 08:13 |
| Last Modified: | 16 Sep 2026 08:13 |
| URI: | https://hau.repository.guildhe.ac.uk/id/eprint/18423 |
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