A Method for Estimating Winter Wheat Height Using UAV Point Cloud Data Enhanced by Density Consistency Filtering

Abstract

Point clouds generated by Structure from Motion (SfM) are often affected by significant noise caused by plant movement, as well as missing ground points caused by canopy occlusion. This reduces the quality of canopy and terrain extraction and makes it challenging to accurately estimate the height of crops from Unmanned Aerial Vehicle (UAV) images. To overcome these limitations, this study introduces a Density Consistency Filtering (DCF) algorithm, which adaptively models local density continuity to distinguish between valid points and noisy points. It effectively preserves local canopy structures while removing clustered noise. Furthermore, a color-spatial interpolation scheme based on ExG-RANSAC is developed to reconstruct missing ground points under dense canopies. Evaluated on six datasets from May to June 2019 covering key stages of winter wheat growth, the method achieved an RMSE of 7.7 cm, MAE of 6.2 cm, and R² of 0.91. After the early stem elongation stage, the method achieved an RMSE of 4.9 cm. The approach significantly improves estimation accuracy of the late growth stages, demonstrating strong potential for precision agriculture applications.

Plant phenotyping relevance

UAV点群から冬小麦の草丈を推定するためのノイズ除去・地面点再構成手法を開発し、複数データセットで精度検証しており、植物形質取得手法が研究の中心である。

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