An adaptive learning method for the fusion information of electronic nose and hyperspectral system to identify the egg quality

Abstract

Data fusion technology based on the multi-sensor system can obtain the holistic properties of samples. However, multi-sensor data fusion will bring more redundant information, which will lead to low classification performance. In this work, a multi-data-fusion-attention network (MDFA-Net) is proposed, combined with the electronic nose (e-nose) and hyperspectral system to identify the egg quality. Firstly, the gas information and spectral information of eggs are obtained under different feeding conditions. Secondly, a feature adaptive learning (FAL) unit is designed to select effective information and enhance the ability of feature expression. Thirdly, based on the FAL unit, a decision network is formed to identify the fusion information of e-nose and hyperspectral system. Finally, compared with other deep learning network models, the accuracy of MDFA-Net is 99.88%, the precision is 99.87%, the recall is 99.88%, and the F₁-score is 99.90%, which shows better classification performance and stability.