Monitoring of corn leaf area index based on multispectral remote sensing of UAV

In order to achieve a rapid estimation of the leaf area index (LAI) of maize, this study explores a more efficient monitoring model for maize LAI estimation based multispectral remote sensing of unmanned aerial vehicle (UAV).

This study focused on maize plants throughout their entire growth cycle. Multispectral imagery of maize plants was acquired using UAV, and maize LAI were collected in field. The quantitative relationship between vegetation index and maize LAI was investigated using multispectral information to select relevant vegetation indices. Multiple linear stepwise regression, support vector machine regression (SVM), random forest regression (RF), and a random forest algorithm optimized using whale optimization algorithm (WOA-RF) were used to construct maize LAI prediction models, respectively. The best prediction model was selected on the basis of comparison.

The vegetation indices of NDVI, NDRE, EVI and CIG were highly correlated with LAI (P < 0.01). The models of multiple linear regression, SVM, RF, and WOA-RF were constructed, with R-squared values of 0.873 2, 0.878 0, 0.917 7, and 0.940 8 respectively, and the root mean square error (RMSE) values of 0.277 5, 0.236 5, 0.209 0, and 0.128 7 respectively.

The prediction model of maize LAI based on WOA-RF provides a high level of accuracy, which can meet the requirement for maize production. It can be used to guide planting management of maize during the growth period.