霉变小麦气相色谱–离子迁移谱的宽度学习检测模型

Yao Qin, Fei Yu Lian, Quan Pan, Yuan Zhang

科研成果: 期刊稿件文章同行评审

摘要

The commonly used methods for wheat mildew detection have some problems such as complicated detection procedures and poor environmental adaptability. Because of this situation, the high-sensitivity gas chromatography-ion migration spectrum (GC-IMS) was applied to detect early mildew of wheat, and the wheat samples with different mildew degrees were tested by using the GC-IMS and classified by using a broad learning model (BLN). To improve the classification accuracy of the broad learning model, a spatial attention mechanism (SAM) is introduced into the model, and the feature information and structure information of nodes are used to calculate attention weight and extract more important feature information. Experimental results show that compared with existing deep learning models, the training time of the proposed model is greatly reduced. In the case of a small number of samples, the early recognition accuracy (AUC) of mildew wheat was also improved, which effectively solved the problem of over-fitting. The experiment also proved the effectiveness of the GC-IMS combined with the BLN-SAM model in the early detection of wheat mildew.

投稿的翻译标题A broad learning detection model on gas chromatography-ion migration spectrum of mildew wheat
源语言繁体中文
页(从-至)1585-1594
页数10
期刊Kongzhi Lilun Yu Yingyong/Control Theory and Applications
40
9
DOI
出版状态已出版 - 9月 2023

关键词

  • broad learning
  • fingerprint spectrum
  • gas chromatography-ion mobility spectrum
  • mildew wheat
  • spatial attention mechanism

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