霉变小麦气相色谱–离子迁移谱的宽度学习检测模型

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.