A multi-kernel channel attention combined with convolutional neural network to identify spectral information for tracing the origins of rice samples

Rice is a primary food consumed daily by many people, and different samples of rice often show disparate quality levels due to different production environments. In the rice market, it is common to sell low-quality rice with high-quality origin labels. As a nondestructive testing technology, spectral analysis has been widely used in food quality supervision. In this work, a deep learning method was developed and combined with a hyperspectral imaging system to achieve a quality-based identification of rice samples from different origins. First, the hyperspectral system was used to obtain spectral information of rice samples from five different origins. Then, a multi-kernel channel attention (MKCA) was proposed to focus on the deep features of the spectral information. Finally, based on the classical deep learning network, combined with MKCA, the spectral characteristics of rice samples from different origins were effectively identified. The results showed that MKCA combined with the LeNet-5 network structure achieved 97.40% accuracy, 97.63% precision, 97.78% recall, and 97.70% F-1-score. It provides an effective technical method for tracing rice.

Automated summary

In this study, a deep learning method combined with a hyperspectral imaging system was developed for quality-based identification of rice samples from different origins. By focusing on the deep features of spectral information, the spectral characteristics of rice samples from different origins were effectively identified. This provides an effective technical method for tracing rice.