Dynamic whole-life cycle measurement of individual plant height in oilseed rape through the fusion of point cloud and crop root zone localization

Abstract

Plant height (PH) of oilseed rape, as a crucial phenotypic indicator, provides essential data for seedling diagnosis and breeding selection when accurately monitored throughout the life cycle of individual plants. However, it is a challenge to obtain precise PH measurements as rapeseed leaves and other crops shade each other after flowering. In this study, a rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field. The point cloud data of oilseed rape during the regreening stage was segmented using an improved fast Euclidean clustering algorithm, followed by extraction of the root collar region via an objective function. Centered on the identified root collar region, an adaptive plant envelope area (PEA) was generated based on the distance between adjacent plants to isolate individual rapeseed specimens. Within the PEA corresponding to each plant’s root collar region, the ground position during sowing and the canopy apex at distinct growth stages were precisely localized, enabling automated extraction of individual PH across the full life cycle. The coefficient of determination (R²) between the algorithm and the manual measurement results at 140, 150 and 165 days after sowing were 0.9742, 0.9667, and 0.9208, respectively. And Root Mean Square Error (RMSE) between the algorithm and the manual measurement results at 140, 150 and 165 days were 0.038, 0.043 and 0.061 m, respectively. These results confirm that integrating BeiDou positioning with 3D point cloud processing achieves high-precision phenotyping of crop height dynamics. Furthermore, PHs were applied to frost damage and lodging susceptibility analysis, which indicate that the growth rate of rapeseed slows down as the severity of frost damage increases, and plants that reach a height of approximately 1 m during the flowering stage are prone to lodging after rainfall. These results have the potential to provide guidance for frost damage assessment and variety selection in smart agriculture applications.

Plant phenotyping relevance

LiDAR・BeiDou搭載プラットフォームと3D点群処理により、個体の生育全期間の草丈を自動抽出する手法を開発し、手測定と検証しているため、植物フェノタイピング手法が中心である。

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