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Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud
| Content Provider | MDPI |
|---|---|
| Author | Liu, Yunling Zhang, Guoli Shao, Ke Xiao, Shunfu Wang, Qing Zhu, Jinyu Wang, Ruili Meng, Lei Ma, Yuntao |
| Copyright Year | 2022 |
| Description | 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 $R^{2}$ (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. |
| Starting Page | 893 |
| e-ISSN | 20734395 |
| DOI | 10.3390/agronomy12040893 |
| Journal | Agronomy |
| Issue Number | 4 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-04-07 |
| Access Restriction | Open |
| Subject Keyword | Agronomy Remote Sensing 3d Point Cloud Region-growing Algorithm Multiscale Tensor Voting Method (mstvm) Phenotyping |
| Content Type | Text |
| Resource Type | Article |
