Apply Physical System Model and Computer Algorithm to Identify Osmanthus Fragrans Seed Vigor Based on Hyperspectral Imaging and Convolutional Neural Network

This study explored the feasibility of using hyperspectral imaging technology and to identify Osmanthus fragrans seeds with different vigor under computer algorithm and physical system. Two varieties of Osmanthus seeds (JinQiGui and RiXiangGui) were artificially aged and then hyperspectral data were collected. Multivariate scattering correction (MSC) was used for spectral preprocessing. The selection of characteristic wavelength was realized by competitive adaptive reweighted sampling algorithm (CARS). The extreme learning machine (ELM) and k-nearest neighbor (KNN) were used to establish the spectral discriminant model, and convolutional neural network was used in the computer image discriminant model. The results show that the ability to recognize different vigor JQG was better than RXG. MSC preprocessing can not only make the data distribution more aggregated, but also effectively improve the accuracy of the model. MSC+CARS combined with discriminant model can be realized close to 100% recognition with fewer bands. Compared with machine learning model, image- depth learning model can get higher model accuracy for different vigor JQG and RXG without complex preprocessing. These results indicate that hyperspectral imaging technology can effectively distinguish different vigor of Osmanthus fragrans seeds based on computer technology and physical system, which is of great significance for future research.