Mapping Wheat Take-All Disease Levels from Airborne Hyperspectral Images Using Radiative Transfer Models

Abstract

Take-all is a root disease that can severely reduce wheat yield, and wheat leaves with take-all disease show a large amount of chlorophyll loss. The PROSAIL model has been widely used for the inversion of vegetation physiological parameters with a clear physical meaning of the model and high simulation accuracy. Based on the chlorophyll deficiency characteristics, the reflectance data under different canopy chlorophyll contents were simulated using the PROSAIL model. In addition, inverse models of spectral reflectance profiles and canopy chlorophyll contents were constructed using a one-dimensional convolutional neural network (1D-CNN), and a transfer learning approach was used to detect the take-all disease levels. The spectral reflectance data of winter wheat acquired by an airborne imaging spectrometer during the filling period were used as input parameters of the model to obtain the chlorophyll content of the canopy. Finally, the results of the distribution of winter wheat take-all disease were mapped based on the relationship between take-all disease and the chlorophyll content of the canopy. The results showed that classification based on the deep learning model performed well for winter wheat take-all monitoring. This study can provide some reference basis for high-precision winter wheat take-all disease monitoring and can also provide some technical method references and ideas for remote sensing crop pest and disease remote sensing mapping.