Differential Study on Estimation Models for Indica Rice Leaf SPAD Value and Nitrogen Concentration Based on Hyperspectral Monitoring

Abstract

Soil and plant analyzer development (SPAD) value and leaf nitrogen concentration (LNC) based on dry weight are important indicators affecting rice yield and quality. However, there are few reports on the use of machine learning algorithms based on hyperspectral monitoring to synchronously predict SPAD value and LNC of indica rice. Meixiangzhan No. 2, a high-quality indica rice, was grown at different nitrogen rates. A hyperspectral device with an integrated handheld leaf clip-on leaf spectrometer and an internal quartz-halogen light source was conducted to monitor the spectral reflectance of leaves at different growth stages. Linear regression (LR), random forest (RF), support vector regression (SVR), and gradient boosting regression tree (GBRT) were employed to construct models. Results indicated that the sensitive bands for SPAD value and LNC were displayed to be at 350–730 nm and 486–727 nm, respectively. Normalized difference spectral indices NDSI (R497, R654) and NDSI (R729, R730) had the strongest correlation with leaf SPAD value (R = 0.97) and LNC (R = −0.90). Models constructed via RF and GBRT were markedly superior to those built via LR and SVR. For prediction of leaf SPAD value and LNC, the model constructed with the RF algorithm based on whole growth periods of spectral reflectance performed the best, with R2 values of 0.99 and 0.98 and NRMSE values of 2.99% and 4.61%. The R2 values of 0.98 and 0.83 and the NRMSE values of 4.88% and 12.16% for the validation of leaf SPAD value and LNC were obtained, respectively. Results indicate that there are significant spectral differences associated with SPAD value and LNC. The model built with RF had the highest accuracy and stability. Findings can provide a scientific basis for non-destructive real-time monitoring of leaf color and precise fertilization management of indica rice.

Keywords

• hyperspectral
• rice
• SPAD value
• leaf nitrogen concentration (LNC)
• machine learning algorithm