Ultra-lightweight dynamic attention network combined with gas sensor for distinguishing the quality of rice

Abstract

Rice can provide humans with basic energy and nutrients, and its quality has attracted great attention. Under different storage conditions, the rice quality is different. In the work, a depth discrimination model, ultra-lightweight dynamic attention network (ULDAN), is proposed to realize rice quality identification combined with electronic nose (e-nose) technology. Firstly, e-nose is used to collect rice gas information under different humidity conditions. Secondly, Ultra-lightweight dynamic convolution block (ULDC) is proposed to extract e-nose data features, which changes the traditional convolution kernel calculation method to enhance the representational ability of features. Thirdly, the convolution classification layer (CCL) is introduced to replace the average pooling layer and fully connected layer to compress the parameter amount and improve the classification performance. In conclusion, ULDAN obtains good classification results under different humidity, of which 75% RH is the best. In addition, these results can provide the technology for quality control.