Quantitative Determination of Rice Moisture Based on Hyperspectral Imaging Technology and BCC-LS-SVR Algorithm

摘要

In this study, a method for quantitative determination of rice moisture based on hyperspectral imaging technology was proposed. First, the hyperspectral imaging system in the spectral range of 871-1766 nm was used to collect the hyperspectral images of 120 rice samples of 10 moisture grades. Support vector regression (SVR), least-squares support vector regression (LS-SVR), and bacterial colony chemotaxis least-squares support vector regression (BCC-LS-SVR) models were established to determine the moisture content by using full wavelengths spectra data. Among all the models, the BCC-LS-SVR model showed the best results. To simplify the calibration model, successive projections algorithm (SPA) was used for feature selection and the number of characteristic wavelengths was determined as 25. Principal component analysis (PCA) was used for feature extraction and the cumulative contribution rate of the first six principal components reached 99%, which could reflect most of the information of the full spectra data. Three new regression models based on the selected wavelengths were built and the results were improved obviously. The BCC-LS-SVR-SPA model got the best accuracy in prediction and calibration with Rp2 of 0.980, RMSEP of 0.967%, Rc2 of 0.985 and RMSEC of 0.591%. The overall results from this study demonstrated that hyperspectral image technology is feasible to detect rice moisture.