Evaluation of Growth Recovery Grade in Lodging Maize via UAV-Based Hyperspectral Images

Abstract

Rapid and nondestructive monitoring of the temporal dynamic changes of agronomic traits of lodging maize is crucial for evaluating the growth recovery status. The purpose of this study is to assess the time-series changes in maize growth recovery after lodging using unmanned aerial vehicle (UAV) hyperspectral technology. Based on the Entropy method, canopy height (CH) and canopy coverage (CC) were used to represent the canopy structure index (CSI), while leaf chlorophyll content (LCC) and plant water content (PWC) were used to represent the physiological activity index (PAI). Based on the theory of normal (skewed) distribution, the growth recovery grade (GRG) of lodging maize was divided based on the estimated CSI and PAI values. The main results were as follows: (a) With the advance of days after lodging (DAL), CH was decreased after increasing, while other agronomic traits exhibited a downward trend. (b) The R 2 values for the CH, CC, LCC, and PWC estimation model were 0.75, 0.69, 0.54, and 0.49, respectively, while the MAPE values were 14.03%, 8.84%, 16.62%, and 6.22%, respectively, in the testing set. (c) The growth recovery of lodging maize was classified using the threshold based on estimated CSI and PAI, achieving an overall accuracy of 77.68%. Therefore, the method for evaluating maize growth recovery after lodging proved effective in monitoring lodging damage. This study provided a reference for the efficient and nondestructive monitoring of growth recovery in lodging maize using UAV-based hyperspectral images.