# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 5 Conclusions  
In this paper, we presented a novel method for determining grasps for robotic grasping of plant cuttings from a flat surface such as a conveyor belt. The method consists of using a CNN for handling plant occlusions when segmenting individual plant cuttings and detection of optimal grasps based on the segmentation results of the CNN. We compared the performance of the used CNN, AdaptIS, using the custom-made occluded plants dataset with two different levels of occlusion severity. The results show that AdaptIS can accurately segment the overlapping plants and its performance is robust at various levels of occlusion. Moreover, although precise segmentation of the stem of a plant cutting represents a challenging task for the segmentation networks, the achieved segmentation result of AdaptIS for this part of the plant cuttings was complete and free from any discontinuities. AdaptIS segmented the target classes for the robotic grasping (Target Cuttings and Singularized Cuttings) with 90% accuracy according to the SQ metric, which is promising for the purpose of our intended robotic grasping. Our CIP-based method for grasp detection of plants achieved 94% for the rectangular metric, and in fact, 50% of the predictions had the highest accuracy in the evaluation range, i.e., an angular deviation of 5 degrees and an IoU of 0.75. We have shown the feasibility of our approach on a plant genus with an irregular object shape similar to many other plant genera in nature so that our method could build a basis for different applications in the food and agricultural industries.  
Our future work will focus on the implementation of the presented method in a robotic pick-and-place system and conducting field tests of the system, with the objective of picking the identified target plant cuttings from the pile of plant cuttings to enable their sorting and placing them to another station of a robotic plant propagation system. In this way, future evaluation of the proposed method will be performed by evaluating the success rate of robotic grasping. In addition to the implementation and subsequent evaluation of the presented method in practice, our future work will also involve its further improvement. Namely, as the occlusion is a 3D feature, using the depth information can improve the  
Figure 10: Examples of grasp detection on the test dataset. (**a**) Normal occlusion image. (**b**) Complex occlusion image.  
occlusion handling accuracy, thus the occlusion handling will be investigated with the segmentation networks using RGB-D images. Moreover, our future work will focus on the generalization of the presented method by extending the training dataset with images of different plant genera, so that the limitation of applicability of the presented methods to only one considered plant genus is overcome.  
Conceptualization, M.M.B. and M.F.; methodology, M.M.B. and M.F.; software, M.M.B.; validation, M.M.B.; formal analysis, M.M.B. and M.F.; investigation, M.M.B.; resources, M.M.B.; data curation, M.M.B.; writing--original draft preparation, M.M.B., M.F.; writing--review and editing, M.M.B., M.F. and D.R.-D.; visualization, M.M.B. and M.F.; supervision, M.M.B., D.R.-D. and K.M.; project administration, M.M.B., D.R.-D. and K.M.; funding acquisition, D.R.-D. and K.M. All authors have read and agreed to the published version of the manuscript. This work is part of the research project funded by the Bremer Aufbau-Bank (BAB) and the EFRE (European Funds for Regional Developments) EU Investments in Future of Bremen under contract VE0118B.  
Not applicable.  
The custom dataset for our proposed method due to confidentiality cannot be publicly available.  
The authors declare no conflict of interest.