Assessing the Self-Recovery Ability of Maize after Lodging Using UAV-LiDAR Data

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.

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

“…Particularly, short crop height estimation was found to be an effective approach by using TLS data for the entire growing season [96]. TLSs can effectively capture more ground and crop points over short crops, giving distinct peak amplitudes for plants and underlying ground, whereas for ULSs, diffused LiDAR backscatter was found over the short crops [40]. Generally, segmentation and classification in crop environments is a challenging task when multi-temporal ULS point clouds are available [25,97,98].…”

“…With the availability of affordable LiDAR sensors onboard UASs, the application of ULS data collection over agriculture environments has substantially intensified in recent years [1,19,27,38,39]. To one end, ULSs have frequently been used for plant location, height, leaf area index (LAI), leaf angle, aboveground biomass (AGB), organ-level nitrogen concentration, lodging, and farmland microtopography evaluation with various additional applications [18,19,[39][40][41]. On the other end, ULS operational practices and quality assessment were evaluated in conjunction with photogrammetric-based point clouds and/or comparison between different UAS LiDAR sensors in coastal and forest environments [42][43][44].…”

Evaluating how lodging affects maize yield estimation based on UAV observations

“…Such decreases may be attributed to the natural self-recovery of maize plants and the manual measures taken. The self-recovery of maize plants could change the canopy characteristics (Hu et al, 2021), which leads to less difference between lodged and non-lodged plants. Moreover, manual measures were applied within 3 days after lodging Estimation bias between measured yield and estimated yield, at the (A) tasseling stage (VT), (B) milking stage (R3), and (C) denting stage (R5).…”

Challenges and opportunities of unmanned aerial vehicles as a new tool for crop pest control

“…For electrostatic sprays, the addition of an electrostatic charge to the droplets can assist attraction to the canopy if wind speeds are very low, with mutual repellency of droplets of equal charge Corn [15][16][17] (positive or negative), induction of the opposite charge at the nearest canopy surface and a net improvement in uniformity of coverage on upper and lower leaf surfaces. This so-called 'wrap around' effect is most effective when the charge to mass ratio is high, which in turn tends to be for relatively small droplet size.…”