Assessing maize lodging severity using multitemporal UAV-based digital images

Abstract

Lodging is a common agricultural disaster in the maize growing period that damages photosynthesis and even affects yield, quality and mechanical harvesting. The purpose of this study was to assess maize lodging severity using the changes in variables extracted from unmanned aerial vehicle (UAV) orthophoto images before and after maize lodging. Maize lodging severity included non-lodging, mild lodging, moderate lodging and severe lodging. We extracted the red, green, blue bands, hue, saturation and value (HSV) colour and texture information before and after maize lodging. Variables sensitive to maize lodging severity were selected using correlation analysis and the Boruta algorithm. Two linear regression and three machine learning methods were used to classify maize lodging severity. From the results of the correlation analysis, 27 variables were significantly related to maize lodging severity at the 0.01 level. Saturation and value were highly correlated with maize lodging severity, with r values of − 0.92 and 0.81, respectively. In addition, 22 variables sensitive to maize lodging severity were selected by the Boruta algorithm. Among the five estimation models built by using sensitive variables, each estimation model performed well, with R2 higher than 0.6. For the linear regression models, the estimation accuracy of the multiple linear regression (MLR) model (R2 = 0.89 and RMSE = 0.84 for the training set; R2 = 0.9 and RMSE = 0.35 for the testing set) was higher than that of the partial least squares regression (PLSR) model. For nonlinear regression models, both the R2 of the training set and testing set for each model were above 0.8. The random forest (RF) estimation model had the highest accuracy among the five models, with an overall accuracy of 89.47% and Kappa coefficient of 0.84. This study indicated that UAV digital images could be used to obtain lodging information over a large number of maize breeding plots, which may be helpful to assist breeders in quickly picking out lodging-resistant maize varieties.