A comparative analysis of 3D point clouds and crop surface models for rice plant height estimation using UAV-SfM

Abstract

Accurate estimation of crop plant height using unmanned aerial vehicles (UAVs) is essential for field-scale crop monitoring and phenotyping. Most previous studies using UAV-based structure-from-motion (SfM) photogrammetry have relied on raster-based crop surface models (CSMs) and have evaluated their performance using accuracy metrics such as the coefficient of determination ( R 2 ) and root mean square error (RMSE). However, such evaluations provide limited insight into how estimation behavior varies across space and time, particularly during dynamic crop growth stages. To address this gap, this study conducted a time-series comparison of rice plant height estimates derived from UAV-SfM-generated dense point clouds (DPCs) and raster-based CSMs in farmer-managed paddy fields in Cambodia, which are characterized by heterogeneous micro-environmental conditions. Rice plant height was measured throughout the growing season and UAV-derived estimates were evaluated using regression analysis, analysis of covariance, and canopy cover dynamics. In the pooled analysis, both approaches achieved high overall accuracy, with R 2 = 0.92 and RMSE = 7.2 cm for the CSM-based approach and R 2 = 0.90 and RMSE = 8.8 cm for the DPC-based approach. However, time-series analyses revealed that CSM-derived plant height estimates exhibited strong location-dependent variability and sensitivity to early-stage canopy development, whereas DPC-based estimates showed more consistent performance across locations and growth stages. Regression coefficients derived from CSM-based estimates varied significantly among locations, whereas those from DPC-based estimates did not, suggesting that point-based representations may provide more spatially consistent estimation behavior under heterogeneous field conditions. By explicitly considering temporal dynamics, canopy development, and data representation, this study highlights the limitations of current raster-based UAV-SfM workflows for structurally complex crop canopies and suggests that DPC-based approaches may offer a useful complementary representation for crop monitoring and phenotyping, particularly when spatial consistency across heterogeneous field conditions is important.

Plant phenotyping relevance

UAV-SfMによるイネの草丈推定手法を、3D点群と作物表面モデルで時系列比較・検証しており、表現形式と技術性能の評価が研究の中心です。

Code and data availability

The paper's UAV-SfM imagery, dense point clouds, CSM/DSM products, and plant height measurements are not deposited in any public repository. The data availability statement only promises raw data from the authors upon request, and no author code or dataset URL is provided. The supplementary material link is generic and

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