Rapeseed Yield Estimation Using UAV-LiDAR and an Improved 3D Reconstruction Method

Abstract

Quantitative estimation of rapeseed yield is important for precision crop management and sustainable agricultural development. Traditional manual measurements are inefficient and destructive, making them unsuitable for large-scale applications. This study proposes a canopy-volume estimation and yield-modeling framework based on unmanned aerial vehicle light detection and ranging (UAV-LiDAR) data combined with a HybridMC-Poisson reconstruction algorithm. At the early yellow ripening stage, 20 rapeseed plants were reconstructed in 3D, and field data from 60 quadrats were used to establish a regression relationship between plant volume and yield. The results indicate that the proposed method achieves stable volume reconstruction under complex canopy conditions and yields a volume–yield regression model. When applied at the field scale, the model produced predictions with a relative error of approximately 12% compared with observed yields, within an acceptable range for remote sensing–based yield estimation. These findings support the feasibility of UAV-LiDAR–based volumetric modeling for rapeseed yield estimation and help bridge the scale from individual plants to entire fields. The proposed method provides a reference for large-scale phenotypic data acquisition and field-level yield management.

Plant phenotyping relevance

UAV-LiDARによる3D再構成とキャノピー体積推定を開発・検証し、植物体積および収量という植物形質を推定する手法が研究の中心である。

Code and data availability

The supplied blocks describe UAV-LiDAR point clouds, 20 reconstructed rapeseed plants, and 60-quadrat yield data, but contain no data availability statement, repository deposit, or author code/model release. No paper-specific public asset is identified.

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