Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

Moisture content detection has guiding significance for the storage and quality detection of rice. To detect moisture content rapidly and non-destructively, hyperspectral imaging technology (400-1000 nm) was employed to analyze rice with different moisture content, and Savitzky–Golay mixed standard normalized variable algorithm (SG-SNV) was used for spectral data pretreatment. Furthermore, a modified supervised locality preserving projections (MSLPP) method was proposed to extract spectral features. The modeling results showed that MSLPP had better spectral feature extraction performance. Finally, to improve prediction accuracy, the equilibrium slime mold algorithm (ESMA) was introduced to obtain the optimal parameters (c, g) of the support vector regression (SVR) model. And MSLPP–ESMA–SVR model had higher prediction accuracy and stronger robustness, with R²_p reaching 0.9755 and root mean square error of prediction reaching 0.8597%. Therefore, hyperspectral imaging technology combined with MSLPP–ESMA–SVR model is feasible to detect rice moisture content.