Growth Monitoring of Weak Gluten Wheat Using Visible and Multispectral UAV Imagery

Abstract

The quality of weak-gluten wheat is easily affected by management methods of field cultivation. U nmanned aerial vehicle (UAV) remote sensing technology can provide technical support for the optimization of cultivation management plan by dynamically monitoring the growth of wheat canopy. In this study, the digital and multispectral cameras mounted on UAV were used to capture canopy images of wheat during key growth stages. The visible and multispectral vegetation indexes of 10 kind of wheat varieties were calculated. The correlation between 13 vegetation indexes and ground-measured chlorophyll content SPAD was analyzed. The results showed that the vegetation index can effectively monitor the change of wheat growth. Among these vegetation indexes, the correlation between the visible light Excess Green index (ExG) and SPAD value is the highest, the determination coefficient R2 is 0.659. The multi-spectral normalized difference vegetation index (NDVI) has the best correlation with SPAD value, the R2 is 0.692. To choose the more suitable sensor for effective assessing the change of wheat growth, the ExG-SPAD and NDVI-SPAD inversion models were established based on the optimal vegetation indexes of these two sensors in midterm and late growth stage. The results shown that the R2 and RMSE of SPAD inversion model at the midterm growth stage were superior than those of late developmental period. Moreover, NDVI-SPAD model obtained more accurate result at midterm growth stage, the R2 and the root mean square error (RMSE) are 0.717 and 1.878, respectively. In summary, the results of this study can provide important technical support for the production plan of weak-gluten wheat in the middle and lower reaches of the Yangtze River. It also helps to promote the further application of remote sensing technology in wheat breeding and cultivation management.