Comparing different machine learning methods for maize leaf area index (LAI) prediction using multispectral image from unmanned aerial vehicle (UAV).

To make an accurate estimation of leaf are index (LAI) based on machine learning methods and images from UAV, we compared the several mainstream machine learning methods for maize LAI prediction, such as Artificial Neural Network method (ANN), Gaussian Process Regression method (GPR), Support Vector Regression method (SVR), and Gradient Boosting Decision Tree (GBDT). For this purpose, field experiments that considering apply of different amount of organic fertilizer, different amount of inorganic fertilizer, different amount of crop residue, and different planting density were carried out. Based on these experiments, field campaign were conducted to obtain UAV multispectral images and LAI data at different growth stages in maize. Based on above data, firstly, correlation analysis was used to select LAI-sensitive spectral indices, and then the Partial Least Squares Regression method (PLSR) and ANN, GPR, SVR, GBDT were coupled to design the LAI prediction models, respectively, and their performance for LAI prediction were compared. The results showed that the LAI prediction model constructed by PLSR+GBDT method had the highest accuracy and the best stability. The models of R² and RMSE values were 0.90 and 0.25, and the verified R² and RMSE values were 0.90 and 0.29 during validation, respectively. The model based on PLSR+GPR model was followed, with R² and RMSE values of 0.86 and 0.30 during calibration, and R² and RMSE values of 0.89 and 0.29 during validation, respectively. Besides, it had faster training speed and could give the uncertainty of the prediction. The model designed by PLSR+ANN method had R² and RMSE values of 0.85 and 0.31 during calibration, and R² and RMSE values of 0.89 and 0.30 during validation, respectively. The model designed by PLSR+SVR method had R² and RMSE values of 0.86 and 0.32, and R² and RMSE values of 0.90 and 0.33, respectively. Therefore, PLSR+GBDT method and PLSR+GPR method are recommended as the optimal methods for designing maize LAI prediction models. [ABSTRACT FROM AUTHOR]