A Patch-Aligned Multimodal Deep Learning Framework for Non-Destructive Freshness Monitoring of Postharvest Mushrooms Using Hyperspectral Imaging.

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.