Maize seed fraud detection based on hyperspectral imaging and one-class learning

文献类型： 外文期刊

第一作者： Zhang, Liu

作者： Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Wu, Jianwei;Wu, Jianwei

作者机构：

关键词： Fraud detection; Maize seeds; Hyperspectral imaging; One -class learning; Deep learning

期刊名称：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE （ 影响因子：8.0； 五年影响因子：7.4 ）

ISSN： 0952-1976

年卷期： 2024 年 133 卷

页码：

收录情况： SCI

摘要： Premium maize varieties are the focus of attention of farmers, breeders, food manufacturers, and people in other industries. Maize seed fraud causes huge financial losses to these industries and many varieties are difficult to distinguish due to their similar appearance. Hyperspectral imaging, as a powerful tool for rapid non-destructive testing, combined with traditional pattern recognition/classification algorithms, has had many successful reports in seed variety identification. In practice, however, fake varieties are too complex to enumerate and a new fake variety may be developed at any time, posing a serious challenge to existing variety identification models. In view of this, this paper proposes a deep one-class learning (OCL) network for seed fraud detection. Specifically, it trains a hypersphere with minimum-volume that can enclose the real variety and isolate all fake varieties outside the hypersphere. In order to improve the performance and stability of the model, the spectral and spatial in-formation of seeds are fused, and a band attention module is used to amplify the weights of the effective bands to suppress the interference from redundant bands. The experimental results show that our method has a mean accuracy of 93.70% for receiving real varieties and 94.28% for rejecting fake varieties, which is superior to several existing state-of-the-art OCL models.

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