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

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 v ²) 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.

Plant phenotyping relevance

UAV-LiDARによるサトウキビ群落LAIの取得・推定手法を開発し、特徴量選択と機械学習モデルの性能評価まで行っており、フェノタイピング手法が中心である。

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