Abstract
Lodging is one of the main problems in maize production. Assessing the self-recovery ability of maize plants after lodging at different growth stages is of great significance for yield loss assessment and agricultural insurance claims. The objective of this study was to quantitatively analyse the effects of different growth stages and lodging severity on the self-recovery ability of maize plants using UAV-LiDAR data. The multi-temporal point cloud data obtained by the RIEGL VUX-1 laser scanner were used to construct the canopy height model of the lodging maize. Then the estimated canopy heights of the maize at different growth stages and lodging severity were obtained. The measured values were used to verify the accuracy of the canopy height estimation and to invert the corresponding lodging angle. After verifying the accuracy of the canopy height, the accuracy parameter of the tasselling stage was R2 = 0.9824, root mean square error (RMSE) = 0.0613 m, and nRMSE = 3.745%. That of the filling stage was R2 = 0.9470, RMSE = 0.1294 m, and nRMSE = 9.889%, which showed that the UAV-LiDAR could accurately estimate the height of the maize canopy. By comparing the yield, canopy height, and lodging angle of maize, it was found that the self-recovery ability of maize at the tasselling stage was stronger than that at the filling stage, but the yield reduction rate was 14.16~26.37% higher than that at the filling stage. The more serious the damage of the lodging is to the roots and support structure of the maize plant, the weaker is the self-recovery ability. Therefore, the self-recovery ability of the stem tilt was the strongest, while that of root lodging and root stem folding was the weakest. The results showed that the UAV-LiDAR could effectively assess the self-recovery ability of maize after lodging.
Highlights
Owing to global warming, the world has frequently experienced extreme weather in recent years, which has seriously impacted agricultural production
The results showed that the plant height directly derived from the unmanned aerial vehicle (UAV) RGB point cloud was highly correlated with ground real data R2 = 0.90, root mean square error (RMSE) = 0.12 m
Through the processing and analysis of point cloud data, 21 groups of canopy height data at the tasselling stage and filling stage were obtained respectively. We compared it with the average value of the measured plant height, to verify the accuracy of obtaining canopy height from LiDAR data
Summary
The world has frequently experienced extreme weather in recent years, which has seriously impacted agricultural production. China Blue Book on Climate Change (2020), from 1951 to 2019, extreme heavy precipitation events in China showed an increasing trend, so too the yearly accumulative total rainstorm precipitation (daily precipitation ≥ 50 mm) and the number of days has increased on average by 3.8% every 10 years [2]. Extreme weather, such as heavy rain and strong winds, is the main cause of maize lodging, which mainly occurs from July to September, which is the growth period of maize. During the vegetative growth stage, according to the number of leaves of the maize plants, they are subdivided into nine categories: VE—Emergence; V1—1st-Leaf; V2—2nd-Leaf; V4—
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