Enhanced classification of wheat disease: In-depth analysis of plant volatile organic compounds based on PTR-MS with prior knowledge and convolutional neural network

文献类型： 外文期刊

第一作者： Wang, Ke

作者： Wang, Ke;Wang, Ke;Wang, Yueting;Yang, Guiyan;Huang, Xuejiao;Ma, Shixiang;Hao, Lianglin;Jiao, Leizi;Zhao, Chunjiang;Dong, Daming;Wang, Ke;Wang, Yueting;Yang, Guiyan;Huang, Xuejiao;Ma, Shixiang;Hao, Lianglin;Jiao, Leizi;Zhao, Chunjiang;Dong, Daming;Liu, Ziyi

作者机构：

关键词： Volatile organic compounds; Proton-transfer-reaction mass spectrometry; Wheat disease; Convolutional neural network; Prior knowledge

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 237 卷

页码：

收录情况： SCI

摘要： Wheat diseases pose a significant threat to global food security by severely reducing crop yields. Rapid, accurate, and reliable identification of wheat infection status, disease types, and disease severity levels is essential for effective disease management. Volatile organic compounds (VOCs), which act as early indicators of plant stress, play a critical role in the early detection and diagnosis of wheat diseases. However, the low concentrations, transient nature, and complex composition of VOCs, combined with the dense canopy structure of wheat plants, present considerable challenges for VOC-based disease identification. To overcome these limitations, this study employed proton-transfer-reaction mass spectrometry (PTR-MS) for the rapid detection of wheat diseases, focusing on mitigating fragment ion interference and mass-to-charge ratio overlap during VOCs spectral characterization. A novel feature recombination method was proposed to improve disease identification accuracy. This approach combines prior knowledge-guided feature recombination with convolutional neural networks for feature extraction, enhancing spectral interpretability and reducing feature redundancy, and enabling rapid VOC-based detection of wheat powdery mildew and stripe rust. Experimental validation demonstrates that the proposed method achieves an accuracy of 90.67% in classifying wheat diseases across different severity levels. Importantly, although this method was designed for wheat disease detection, its framework is adaptable and may be extended to other plant health monitoring applications using PTR-MS.

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