A high-throughput plant canopy leaf area index inversion model based on UAV-LiDAR.

Yuming LIANG; Xueyan FAN; Muqing ZHANG; Wei YAO; Xiuhua LI; Zeping WANG; Sifan DONG; Xuechen LI

Return

A high-throughput plant canopy leaf area index inversion model based on UAV-LiDAR.

Author: Yuming LIANG ¹ ; Xueyan FAN ¹ ; Muqing ZHANG ² ; Wei YAO ² ; Xiuhua LI ¹ ; Zeping WANG ³ ; Sifan DONG ¹ ; Xuechen LI ¹
Author Information

1. School of Electrical Engineering, Guangxi University, Nanning 530004, Guangxi, China.
2. State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Nanning 530004, Guangxi, China.
3. Agricultural Science and Technology Information Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, Guangxi, China.
Publication Type:Journal Article
Keywords: leaf area index (LAI); light detection and ranging (LiDAR); machine learning; sugarcane; unmanned aerial vehicle (UAV)
MeSH: Plant Leaves/growth & development*; Saccharum/growth & development*; Algorithms; Unmanned Aerial Devices; Remote Sensing Technology/methods*; Crops, Agricultural/growth & development*
From: Chinese Journal of Biotechnology 2025;41(10):3817-3827
CountryChina
Language:Chinese
Abstract: To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.