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Improving mango cold-damage and bruise detection using thermal imaging and flexible spectral sensing

文献类型： 外文期刊

作者： He, Wenhao 1 ; Huang, Wentao 1 ; Popovic, Tomo 2 ; Zhu, Zhiqiang 3 ; Zhang, Xiaoshuan 1 ;

作者机构： 1.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

2.Univ Donja Gorica, Fac Informat Syst & Technol, Podgorica 81000, Montenegro

3.Tianjin Acad Agr Sci, Natl Engn & Technol Res Ctr Preservat Agr Prod, Tianjin 300384, Peoples R China

4.China Agr Univ, Sanya Res Inst, Sanya 572000, Peoples R China

5.China Agr Univ, Beijing 100083, Peoples R China

关键词： Mango quality inspection; Bimodal data fusion; Integrated learning network; Thermal imaging; Visible/near-infrared spectral sensing

期刊名称：FOOD CONTROL （ 影响因子：6.3； 五年影响因子：6.1 ）

ISSN： 0956-7135

年卷期： 2025 年 172 卷

页码：

收录情况： SCI

摘要： Influenced by chemical processes such as respiration, ethylene production, and oxidation reactions, fruits are prone to cold damage and bruising during storage and transportation. To this end, this paper presents an ensemble learning network based on dual-modal data fusion for real-time detection and assessment of mango quality. On one hand, we developed a non-destructive classification system that integrates thermal imaging technology with flexible visible/near-infrared spectral sensing, capturing the physicochemical characteristics of mangoes both on the surface and internally. On the other hand, the CNN-ATT-BiLSTM-based ensemble learning framework effectively enables data fusion from the feature layer to the decision layer. Results indicate that mangoes can be classified into four categories: bruised, cold-damaged, both cold-damaged and bruised, and normal. The CNN-ATT-BiLSTM network achieved an overall accuracy of 95.24%. Experiments on grading pipelines demonstrated the system's strong stability. This research contributes new methodologies to the development of fruit quality assessment technologies.

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