Lightweight Residual Convolutional Neural Network for Soybean Classification Combined With Electronic Nose

The quality of soybeans from different growing areas is different. It is common for low-quality soybeans to fake high-quality soybeans. This paper proposes a lightweight residual convolutional neural network (LRCNN) combined with an electronic nose (e-nose) to realize soybean quality traceability. Firstly, obtain soybean gas information from different growing areas through the e-nose. Then, according to the characteristics of e-nose detection data, the grouped heterogeneous kernel-based convolution (GHConv) is proposed, which effectively reduces the number of parameters through the combination of grouping and heterogeneous convolution. Finally, the LRCNN is proposed, which reduces the number of network parameters and avoids feature degradation, realizing the high-precision identification of soybean quality differences. In the multi-model comparison, the classification accuracy of the network is 98.37%, recall is 98.20%, and precision is 98.49%. The results show that the LRCNN combined with the e-nose can effectively identify the gas information of soybeans from different growing areas, providing a new method for soybean quality traceability.