# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 4 Discussion
### 1.2.1. Experimental Results and Comparative Analysis
After completing the point-level visual analysis, the experiment further computes organ-level true positives (ITP), false positives (IFP), and false negatives (IFN), and from these derives instance recall, instance precision, and the instance F-measure. Table 5 compares  
Figure 12: Single-leaf visualization for Sorghum 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 blue, red, and yellow to denote correct points (CPs), omitted points (MPs), and false points (FPs), respectively. Figure 13: Single-Leaf visualization of Sorghum 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. the metrics for the proposed algorithm, method [23], and a DBSCAN-based method on six plant samples. From the table above, for Tobacco 1 and Tobacco 2, the proposed method achieves 100% instance recall and 100% instance precision, providing accurate segmentation for every ground-truth leaf. In contrast, method [23] for Tobacco 1 misses one true positive instance, resulting in both recall and precision of 83.34%, whereas the DBSCAN-based approach produces multiple false positives. For Tobacco 2, although method [23] attains 100% recall, its precision is only 83.34%, and DBSCAN again exhibits missed detection. Tomato 1 and Tomato 2 show a similar trend: in Tomato 1, method [23] identifies only a single leaf, leading to 50% recall and precision, while both the proposed method and DBSCAN achieve 100% instance segmentation. In Tomato 2, the proposed method maintains 100% recall and precision, whereas method [23] achieves only 50% precision. For Sorghum 1 and Sorghum 2, the proposed method's instance segmentation performance is comparable to that of method [23], with both attaining instance F-measures of 75% or 100%. However, compared with numerous missed and false detections of DBSCAN, the proposed algorithm demonstrated superior overall stability. Combining the point- and organ-level results shows that the proposed method not only significantly improves the point-level coverage rate (CR) compared with [23] but also achieves the best or equivalent performance in organ-level true-positive identification and instance F-measure. Especially in canopy point clouds with closely spaced or heavily occluded leaves, the FFN-patch-based clustering and segmentation strategy better preserves leaf-edge continuity, reducing both missed and false detections, and thus delivers high-precision segmentation with enhanced stability and robustness. To evaluate scalability and resource efficiency, we measured the inference latency, peak GPU memory usage, and floating-point operations (FLOPs) of the FFN and three representative alternatives (Slic, DBSCAN, and PointNet++). The experimental results are presented in Table 6.