Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665-685 and 705-880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.

Automated summary

This study utilized multitemporal hyperspectral UAV data to identify sensitive wavebands for rice false smut (RFS) and extract RFS-infected areas using two methods. The results showed that the second method had better prediction accuracy and that the infected areas exhibited an expanding trend over time, consistent with the natural development law of RFS.