Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

Abstract

Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R2 (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.

Plant phenotyping relevance

個葉の3D点群セグメンテーション手法を開発し、手動セグメンテーションを基準に評価、葉長・葉面積の推定精度も検証しており、表現型取得が研究の中心である。

Code and data availability

The paper describes sugar beet 3D point cloud segmentation and phenotypic extraction, but no public dataset, image, code, or model repository is disclosed. The Data Availability Statement reads 'Not applicable,' and no author-provided URL for data or code appears in any block. Only generic libraries (sklearn, open3d) 3

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