Objective In order to monitor rice bacterial blight quickly and non-destructively, and guide field operations.

Method High-resolution images of rice canopy under bacterial blight stress were acquired using utilized unmanned aerial vehicles (UAVs). Color features and texture features were extracted from the images, and multiple regression models based on color features, texture features, and the fusion of color and texture features were constructed to predict the infection level of rice bacterial blight. The influence of different ground sampling distances (GSD) on the accuracy of the models was also explored.

Result Compared with the model based on texture features, the monitoring model based on color features (R²=85.9%, RMSE=1.43, RRMSE=19.1%) showed an increase of 2.4 percentage points in the R² and an increase of 4.6 percentage points in RRMSE. Compared with single-feature models, the prediction model based on the fusion of color and texture features (R²=89.6%, RMSE=1.06, RRMSE=15.1%) exhibited significant improvement in accuracy. By constructing models with different GSDs, it was found that when the GSD was 0.2, 0.5 or 0.8 cm, the models achieved higher accuracy with R² all above 80%.

Conclusion The color and texture features extracted from low-altitude remote sensing images captured by UAVs can be used for monitoring rice bacterial blight. The results can provide effective technical support for UAV remote sensing monitoring of rice bacterial blight.