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

Na Li, Zhiwei Hou, Haiyong Jiang, Chongchong Chen, Chao Yang, Yanan Sun, Lei Yang, Tianyu Zhou, Jingyu Chu, Qingzhe Fan and Lijie Zhang ()
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Na Li: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Zhiwei Hou: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Haiyong Jiang: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Chongchong Chen: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Chao Yang: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Yanan Sun: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Lei Yang: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Tianyu Zhou: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Jingyu Chu: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Qingzhe Fan: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
Lijie Zhang: College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China

Agriculture, 2025, vol. 15, issue 21, 1-18

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.

Keywords: UAV-LiDAR; 3D reconstruction; canopy volume; rapeseed yield; HybridMC-Poisson; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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