Rice false smut extraction based on the combination of instability index between classes and correlation coefficient of uav hyperspectral band selection

Rice false smut (RFS) is a late fungal disease mainly occurring on rice panicle in recent years. This research was based on the unmanned aerial vehicle (UAV) hyperspectral remote sensing data. On the basis of genetic algorithm combined with partial least squares to select the feature bands, the correlation coefficient method and Instability Index between Classes method were used to further select the feature bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10% respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the characteristic bands, the sensitive band ranges of rice false smut monitoring were determined, which were 698nm-750nm and 974nm-984 nm.