Monitoring the Rice Panicle Blast Control Period Based on UAV Multispectral Remote Sensing and Machine Learning

The heading stage of rice is a critical period for disease control, such as for panicle blast. The rapid and accurate monitoring of rice growth is of great significance for plant protection operations in large areas for mobilizing resources. For this paper, the canopy multispectral information acquired continuously by an unmanned aerial vehicle (UAV) was used to obtain the heading rate by inversion. The results indicated that the multi-vegetation index inversion model is more accurate than the single-band and single-vegetation index inversion models. Compared with traditional inversion algorithms such as neural network (NN) and support vector regression (SVR), the adaptive boosting algorithm based on ensemble learning has a higher inversion accuracy, with a correlation coefficient (R-2) of 0.94 and root mean square error (RMSE) of 0.12 for the model. The study suggests that a more effective inversion model of UAV multispectral remote sensing and heading rate can be built using the AdaBoost algorithm based on the multi-vegetation index, which provides a crop growth information acquisition and processing method for determining the timing of rice tassel control.