# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 4 Discussion
### 1.2.1. Experimental Results and Comparative Analysis  
Quantitative performance metrics are divided into point- and leaf-level categories to comprehensively and accurately measure the quality of crop phenotyping segmentation. Point-level metrics evaluate a single ground-truth leaf point set (Num_gt), a segmented leaf point set (Num_Seg), and the range of segmentation errors. Figure 7 illustrates the tobacco ground-truth leaf and segmented leaf: subfigure (a) shows the original colorized point cloud of the tobacco leaf, where Num_gt represents all ground-truth leaf points; subfigure (b) shows the segmented leaf point cloud, denoted as Num_Seg; subfigure (c) overlays the two, with blue indicating overlapping points between ground truth and segmentation (correct points, CPs), red indicating ground-truth points that were omitted (omitted points, MPs), and yellow indicating erroneously included points in the segmented leaf (false points, FPs). Leaf-level metrics are based on the point-level single-leaf coverage rate (Cover_rate, CR) to evaluate the accuracy of the segmentation of each leaf. CR is defined as the ratio of overlapping points between the segmented leaf and ground truth to the total number of ground truth leaf points. If a ground-truth leaf has exactly one segmented region with a CR greater than 70%, the leaf is considered a true positive (ITP). If a single segmented region covers multiple ground-truth leaves and each of their CR values exceeds 70%, only one is counted as ITP, and the others are considered false positives (IFP). If a ground-truth leaf does not have any segmented region with a CR of 70%, it is counted as a false negative (IFN). First, this section presents point-level visualizations of single-leaf segmentation results for tobacco, tomato, and sorghum, and then aggregates organ-level metrics to quantify segmentation performance. The single-leaf visualization results for tobacco plant 1, tomato plant 1, and sorghum plant 1 using the two methods are shown in Figures 8-13. In each subfigure, column (a) shows the ground-truth leaf point set (Num_gt), column (b) shows the corresponding segmented leaf point set (Num_Seg), and column (c) uses blue, red, and yellow to denote correct points (CPs), omitted points (MPs), and false points (FPs), respectively. Figure 8: Single-leaf visualization for Tobacco Plant 1 using method [23]. (**a**) shows the ground-truth leaf point set (Num_gt), (**b**) shows the corresponding segmented leaf point set (Num_Seg), and (**c**) uses  
Figure 7: Tobacco Ground-Truth Leaf and Segmented Leaf. (**a**) shows the ground-truth leaf point set (Num_gt), (**b**) shows the corresponding segmented leaf point set (Num_Seg), and (**c**) uses blue, red, and yellow to denote correct points (CPs), omitted points (MPs), and false points (FPs), respectively. Figure 11: Single-leaf visualization for Tomato Plant 2 using the proposed method. (**a**) shows the ground-truth leaf point set (Num_gt), (**b**) shows the corresponding segmented leaf point set (Num_Seg), and (**c**) uses blue, red, and yellow to denote correct points (CPs), omitted points (MPs), and false points (FFs), respectively. Figure 9: Single-leaf visualization for Tobacco Plant 2 using the proposed method. (**a**) shows the ground-truth leaf point set (Num_gt), (**b**) shows the corresponding segmented leaf point set (Num_Seg), and (**c**) uses blue, red, and yellow to denote correct points (CPs), omitted points (MPs), and false points (FPs), respectively. By direct comparison, it is evident that the proposed algorithm preserves the leaf edges and local details more completely. The proportion of CPs (blue) is significantly higher than that of method [23], and in regions of complex occlusion or adjacent leaves with similar textures, the numbers of MPs (red) and FPs (yellow) are noticeably reduced.