文献类型： 外文期刊
作者： Zhang, Liu 1 ; Wei, Yaoguang 1 ; Liu, Jincun 1 ; An, Dong 1 ; Wu, Jianwei 5 ;
作者机构： 1.China Agr Univ, Natl Innovat Ctr Digital Fishery, Beijing 100083, Peoples R China
2.China Agr Univ, Key Lab Smart Farming Technol Aquat Anim & Livesto, Minist Agr & Rural Affairs, Beijing 100083, Peoples R China
3.Beijing Engn & Technol Res Ctr Internet Things Agr, Beijing 100083, Peoples R China
4.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
5.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China
6.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China
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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