# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 4 Results and Discussion
### Grasp Detection  
The presented grasp detection method was applied to 100 images of the test dataset, which included around 250 instances of grasping target classes (Target Cutting and Singularized Cutting). Some features used for grasp detection are related to the resolution of  
Figure 9: Examples of panoptic segmentation results with AdaptIS network on occluded plants dataset. (**a**) Ground truth of normal occlusion images. (**b**) Segmentation results of normal occlusion images. (**c**) Ground truth of complex occlusion images. (**d**) Segmentation results of complex occlusion images. In order to have a clearer visualization of cutting masks, the mask of the Stuff classes (here the image background) is not visualized. the image. Two of these features are the minimum and maximum possible width of the plant cutting stem and the minimum acceptable area of RoI. Since the optimal input size of the AdaptIS for inference was 640 pixels, these features were set for an image resolution of 640 pixels. The range of the cutting stem was set to 2 to 7 pixels, and the smallest acceptable area of the RoI was set to 125 pixels. As mentioned in Section 3.4.3, the CIR value, which is independent of the image resolution, was set to 0.5 and 0.9 for the horizontal and vertical sides of the CIR, respectively. To evaluate the achieved results in grasp detection, we used the rectangle metric [36]. Based on this metric, a predicted grasp is correct if the deviation of grasp orientation between the predicted grasp and the ground truth grasp is less than 30\({}^{\circ}\) and the IoU (intersection over union) value between the predicted rectangle and the ground truth is more than 0.5. To analyze the performance of our grasp detection method, we used the rectangle metric at different orientation angle criteria (5, 15, 30, and 45 degrees) and different IoU criteria (0.25, 0.5, and 0.75). The runtime was 15 ms per image on the local machine with an Intel(r) Xeon(r) Silver 4216 processor. Table 3 presents the evaluation results of grasp detection on the test dataset. As the result in Table 3 shows, the standard rectangle metric for the presented method is 94%, which is a high performance and 50% of the predicted grasps are correct in the most constrained case, namely with an angular deviation of 5\({}^{\circ}\) and an IoU of 0.75, demonstrating the high robustness of our method for grasp detection of plant cuttings. Conversely, the predicted grasps that belong to the least restricted case (angular deviation 45\({}^{\circ}\) and IoU 0.25) lead to successful grasping but also cause rotation of the plant cuttings due to the moderate angular deviation, and the accuracy of our method in this regard is 95%. As the examples in Figure 10 show, the predicted grasps are on the stem with sufficient clearance to accommodate the open gripper as well as with a small angular deviation. In Figure 10a, the segmented Target Cutting at the bottom left corner has a partial segmentation inaccuracy. Despite this inaccuracy, the grasp is correctly predicted, demonstrating the robustness of our method for grasp detection. The stem around the tip of the plant cutting is thinner and softer, thus, grasping and picking the cutting at this point may cause bending the cutting toward the ground, though it will not lead to falling. In Figure 10b, the grasp shown in the bottom left corner is an example of this rare occurrence. The reason that such cases are rare is that the tips of cuttings usually have several dense leaves, thus the stem around the tip of the cutting does not have enough room to accommodate the gripper. For this reason, in the last step of our proposed method, most of the grasp proposals near the tip of the cutting are excluded.