Authors
Yun Li, Ruicheng Qiu, Shuangdi Hou, Min Zhang, Xiang Wang
Published in
Food research international (Ottawa, Ont.). Volume 241. Pages 119749. Oct 01, 2026. Epub Jun 13, 2026.
Abstract
Kiwifruit freshness changes rapidly post-harvest, yet subtle external visual manifestations of physiological deterioration and complex environments hinder precise unimodal assessment. This study proposes MMFNet-K, a Multimodal Fusion Network for kiwifruit freshness classification integrating RGB imaging, hyperspectral imaging (HSI), and electrochemical impedance spectroscopy (EIS). The model utilizes a dual-branch architecture with two-stage fusion involving feature-level and decision-level strategies to achieve deep coupling of cross-modal information. To validate generalization, two logistics scenarios including standard cold chain (4 °C) and ambient retail (25 °C) were simulated across three major cultivars including Hayward, Golden, and Xuxiang to construct a non-destructive dataset. Objective freshness grading standards were established and verified through firmness and soluble solids content analysis. Experimental results demonstrate that MMFNet-K achieved accuracies ranging from 95.6%-99.3% across all six cultivar-temperature combinations, with a macro-average precision of 0.978, a recall of 0.977, and an F1-score of 0.977. Comparative analysis indicates that the model outperforms unimodal methods by 9.13% and surpasses existing mainstream multimodal fusion schemes by 5.43%. These results demonstrate the high robustness of MMFNet-K in complex post-harvest environments and its significant competitive advantage over traditional methods. Furthermore, a practical application scheme was designed based on this model to provide a feasible technical path for kiwifruit freshness grading and contribute to the reduction of waste and losses during post-harvest processes.
PMID:
42562521
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.
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