Estimates of rice lodging using indices derived from UAV visible and thermal infrared images

Rice lodging not only causes difficulty in harvest operations, but also drastically reduces yield. Rice lodging assessment contributes greatly to rice plantation and crop field management. In this study, we collected visible and thermal infrared images with an unmanned aerial vehicle. Then, based on hybrid image analysis and field investigation, we established a comprehensive rice lodging recognition model using a particle swarm optimization and support vector machine algorithm. The results showed that color and texture features were different between lodged and non-lodged rice plants. Moreover, the temperature was distinct between lodging and non-lodging areas, with lodged rice having higher canopy temperature. The developed model based on the visible and thermal infrared images was validated using different Indica and Japonica rice cultivars. The model had a false positives rate and false negatives rate of less than 10%, and estimated lodging rate with an R2 greater than 0.9. These results indicated that combination of visible and thermal infrared images feature significantly increased the rice lodging recognition accuracy. The developed model can be used to monitor rice lodging and estimate the lodging rate.