Inversion of maize leaf nitrogen using UAV hyperspectral imagery in breeding fields

Nitrogen (N) as a pivotal factor in influencing the growth, development, and yield of maize. Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture, based on unmanned aerial vehicle (UAV) remote sensing technology. In this study, the hyperspectral images were acquired by UAV and the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) were measured to estimate the N nutrition status of maize. 24 vegetation indices (VIs) were constructed using hyperspectral images, and four prediction models were used to estimate the LNC and LNA of maize. The models include a single linear regression model, multivariable linear regression (MLR) model, random forest regression (RFR) model, and support vector regression (SVR) model. Moreover, the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields. The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll (NDchl) had the highest prediction accuracy for LNC (R2, R 2 , RMSE, and RE were 0.72, 0.21, and 12.19%, respectively) and LNA (R2, R 2 , RMSE, and RE were 0.77, 0.26, and 14.34%, respectively). And then, 24 VIs were divided into 13 important VIs and 11 unimportant VIs. Three prediction models for LNC and LNA were constructed using 13 important VIs, and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model, in which RFR model had the highest prediction accuracy for the validation dataset of LNC (R2, R 2 , RMSE, and RE were 0.78, 0.16, and 8.83%, respectively) and LNA (R2, R 2 , RMSE, and RE were 0.85, 0.19, and 9.88%, respectively). This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.