Prediction of Soil Total Nitrogen Based on CatBoost Algorithm and Fusion of Image Spectral Features

In order to solve the problem that the internal relationship between external features of color images and soil nutrients is ignored when hyperspectral technology is applied to quantitative detection of soil nutrients, a prediction model of soil total nitrogen content based on image and spectral features was constructed by combining the spectral information and image features of soil, and the prediction ability of image and spectral features fusion for soil total nitrogen content was explored. The hyperspectral images of soil samples were obtained by the laboratory hyperspectral imager, and the spectral information and image characteristics of soil were extracted from the hyperspectral images. The characteristic wavelength of spectral information was selected by using a joint algorithm of uniformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS), and the selected characteristic wavelength was used as soil spectral information. Through correlation analysis, image features with high correlation with soil total nitrogen content were selected. Categorical Boosting (CatBoost) algorithm was applied to the prediction of soil total nitrogen content, and the prediction of soil total nitrogen content based on single spectral information, single image feature and map feature fusion was compared. The results showed that the characteristic wavelengths selected by UVE-CARS joint algorithm were 942 nm, 1 045 nm, 1 199 nm, 1 305 nm, 1 449 nm, 1 536 nm and 1 600 nm, which were consistent with the frequency doubling absorption of nitrogen-containing groups. The image features with high correlation with soil total nitrogen content were angle second moment, energy, inertia moment, gray mean and entropy. The model based on the characteristic wavelength of single spectral information established by CatBoost algorithm finally predicted that the total nitrogen content of soil R2 was 0.832 9 and RMSE was 0.203 3 g/kg, the model based on image features finally predicted that the total nitrogen content of soil R2 was 0.801 7 and RMSE was 0.219 7 g/kg. And the model based on fusion of image and spectral features finally predicted that the total nitrogen content of the soil R2 was 0.866 8, and RMSE was 0.160 2 g/kg, the prediction accuracy was higher than that of single spectral feature and single image feature. Compared with the prediction model based on single spectral feature and single image feature, the prediction model based on hyperspectral atlas feature fusion had better effect, which can provide a method for the prediction of soil total nitrogen content. © 2021, Chinese Society of Agricultural Machinery. All right reserved.