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<p>Precision agriculture requires detailed characterization of pesticide deposition within fruit tree canopies to account for spatial heterogeneity and improve spraying efficiency. Recent advances in LiDAR-based point cloud technology have enabled three-dimensional canopy reconstruction, providing new opportunities to link canopy structure with spray deposition. This study aimed to evaluate the predictive capacity of canopy structural parameters for pesticide deposition coverage using a voxel-based approach and evaluating its practical applications. Handheld LiDAR was used to acquire canopy point clouds, which were voxelized to extract spatial position and leaf area index (LAI). Pesticide deposition was quantified using water-sensitive paper (WSP). A total of 10 peach trees were tested under three fan speeds (1733, 1989, and 2206 r/min), and the data were divided into modeling and validation datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR), and exponential regression (ER) were tested. The PR model achieved the highest predictive accuracy. It reached R² = 0.86 (MAE = 0.08, RMSE = 0.10) at 1733 r/min; R² = 0.82 (MAE = 0.08, RMSE = 0.10) at 1989 r/min; and R² = 0.80 (MAE = 0.09, RMSE = 0.11) at 2206 r/min. Classification analysis of deposition levels showed that different models exhibited strengths in specific deposition ranges, although extreme classes remained difficult to predict. These findings demonstrate that voxel-based canopy characterization can provide reliable predictions of pesticide deposition and support the development of precision spraying strategies in orchards.</p>

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Prediction of pesticide deposition in fruit tree canopies: A voxel-based analysis approach

  • 期刊论文

  • 发表日期：

    2026年3月18日

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  • Kai Zhang,
  • Feng Kang,
  • Ning Wang,
  • Yuheng Cao,
  • Linsen Yang,
  • Yaxiong Wang,
  • Chongchong Chen

摘要

Precision agriculture requires detailed characterization of pesticide deposition within fruit tree canopies to account for spatial heterogeneity and improve spraying efficiency. Recent advances in LiDAR-based point cloud technology have enabled three-dimensional canopy reconstruction, providing new opportunities to link canopy structure with spray deposition. This study aimed to evaluate the predictive capacity of canopy structural parameters for pesticide deposition coverage using a voxel-based approach and evaluating its practical applications. Handheld LiDAR was used to acquire canopy point clouds, which were voxelized to extract spatial position and leaf area index (LAI). Pesticide deposition was quantified using water-sensitive paper (WSP). A total of 10 peach trees were tested under three fan speeds (1733, 1989, and 2206 r/min), and the data were divided into modeling and validation datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR), and exponential regression (ER) were tested. The PR model achieved the highest predictive accuracy. It reached R² = 0.86 (MAE = 0.08, RMSE = 0.10) at 1733 r/min; R² = 0.82 (MAE = 0.08, RMSE = 0.10) at 1989 r/min; and R² = 0.80 (MAE = 0.09, RMSE = 0.11) at 2206 r/min. Classification analysis of deposition levels showed that different models exhibited strengths in specific deposition ranges, although extreme classes remained difficult to predict. These findings demonstrate that voxel-based canopy characterization can provide reliable predictions of pesticide deposition and support the development of precision spraying strategies in orchards.