Abstract
This study aims to develop an effective and reliable method for estimating rice quality indices and yield, addressing the growing need for rapid, non-destructive, and accurate predictions in modern agriculture. Field experiments were conducted in 2018 at the Suiling Water Conservancy Comprehensive Experimental Station (47°27' N, 127°06' E), using Longqingdao 3 as the test variety. Measurements included the leaf area index (LAI), chlorophyll content (SPAD), leaf nitrogen content (LNC), and leaf spectral reflectance during the tillering, jointing, and maturity stages. Based on these parameters, spectral indicators were calculated, and univariate linear regression models were developed to predict key rice quality indices. The results demonstrated that the optimal R(2) values for brown rice rate, moisture content, and taste value were 0.866, 0.913, and 0.651, with corresponding RMSE values of 0.122, 0.081, and 1.167. After optimizing the models, the R(2) values for the brown rice rate and taste value improved significantly to 0.95 (RMSE: 0.075) and 0.992 (RMSE: 0.179), respectively. Notably, the spectral index GM2 during the jointing stage achieved the highest accuracy for yield prediction, with an R(2) value of 0.822. These findings confirm that integrating multiple indicators across different growth periods enhances the accuracy of rice quality and yield predictions, offering a robust and intelligent solution for practical agricultural applications.