Assessing rice lodging using UAV visible and multispectral image

Rice lodging may result in serious yield loss. Manual in-situ assessments of lodging are inefficient and inaccurate. Therefore, this paper explores the potential of unmanned aerial vehicle (UAV) image in evaluating rice lodging. Multispectral and red-green-blue (RGB) cameras mounted on UAV platforms were used to acquire images of two rice paddies in Shanghai, China. The image features of non-lodged and lodged rice, including their spectral reflectance, vegetation indices, texture, and colour, were extracted and analysed to optimize the indicators for lodging detection. Rice lodging detection models based on the selected image features were built to discriminate between non-lodged and lodged rice. Results revealed that the reflectances at the green and red-edge bands are the most common optimal indicators of lodged rice in the multispectral images. Moreover, the mean texture of the green channel and lightness value were determined to be the optimal indicators of rice lodging in the RGB image. The accuracies of the proposed lodging detection models exceeded 90%, and the multispectral lodging detection model was proved to be more accurate than the corresponding RGB model. Our results confirm that UAV-based remote sensing can play an important role in assessing rice lodging.