A deep learning method combined with electronic nose to identify the rice origin

Abstract

The rice quality is directly affected by the natural environment in which it is grown. It is common to pass off poor quality as good quality rice sold in rice markets. Therefore, an effective rice quality detection method should be proposed. This work proposes a deep learning method combined with an electronic nose (e-nose) to realize the classification of rice origin, and then realize the detection of rice quality. Firstly, channel-spatial collaborative attention (CSCA) is proposed to adaptively focus on key features of gas information. Secondly, three shallow convolutional neural network (CNN) structures are introduced to achieve gas information classification. Finally, CSCA and three CNN structures are combined to construct a classification network for rice gas recognition. The results show that in the comparison of multi-structure models, LeNet5-CSCA has the best classification performance, with a classification accuracy of 97.70%, a precision of 97.45%, a recall of 97.67%, and a F1-score of 97.56%.