Estimation of Nitrogen Status in Zanthoxylum armatum var. novemfolius Using Machine Learning Algorithms and UAV Hyperspectral and LiDAR Data Fusion.

Abstract

Accurate monitoring of nitrogen (N) status is critical for precision N management and optimizing the yield and quality of Zanthoxylum armatum var. novemfolius (ZA). However, individual sensors often struggle to simultaneously capture the biochemical variations and complex canopy structural changes of ZA. Therefore, field experiments were conducted over two consecutive years, applying four N-application rates (0, 150, 300, and 450 kg N ha -1 ) to ZA. At each phenological stage, hyperspectral imagery and LiDAR point clouds were collected via three UAV flight altitudes (60 m, 80 m, and 100 m), and canopy nitrogen concentration (CNC) and aboveground nitrogen accumulation (AGNA) were measured. This study developed a framework by synergistically fusing UAV-derived hyperspectral imaging (HSI) and LiDAR data for CNC and AGNA monitoring. Results showed that the response of nitrogen status indicators to fertilization was phenology-specific: CNC showed no significant difference ( p > 0.05) among treatments during the vigorous vegetative growth stage (VGS) but differed significantly ( p p (732, 879) and NDSI (560, 690) as the optimal CNC indicators at VGS and FES, respectively (r = 0.83 and 0.93), whereas the NDSI (711, 986) and NDSI (515, 736) were identified as the optimal AGNA indicators at VGS and FES, respectively (r = 0.91 and 0.71). Across all phenological stages, Random Forest Regression consistently delivered the highest accuracy for CNC (R 2 = 0.93-0.98, RMSE = 0.87-1.02 g kg -1 ) and AGNA (R 2 = 0.95-0.97, RMSE = 1.92-2.55 g plant -1 ), outperforming MLR, PLSR, and SVR. This synergistic framework provides a high-precision, non-destructive methodology for the precision N monitoring of woody crops.

Plant phenotyping relevance

UAVハイパースペクトル画像とLiDARの融合、および機械学習により、植物の窒素濃度・窒素蓄積量を非破壊推定する方法を開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

The paper reports UAV hyperspectral/LiDAR phenotyping of Zanthoxylum armatum nitrogen status, but no public phenotype dataset, raw imagery/point clouds, analysis code, or trained models are deposited. The Data Availability Statement only offers inquiries to the corresponding author, and the MDPI supplement (Tables S1–S

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