# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 2 Related Work
The method given in [14] is a proposal-free method that is capable of dealing with complex shapes with a high number of crossovers. It has shown good performance for small biomedical applications with datasets having complicated shapes and dense crossovers with only one class of objects method rather than a multi-class dataset such as that of our dataset. In addition, this method is computationally expensive, thus the images should be drastically reduced in resolution size, and this means the loss of effective features for segmentation of plant cuttings, which are characterized by a thin stem. Because of these shortcomings, this method is not suitable for the application presented in this paper, segmentation of overlapped plant cuttings placed on flat surfaces. The network named AdaptIS, presented in [13], is the network that we used in the work presented in this paper, and it is an end-to-end class agnostic method. It takes the raw image as input along with a point on the object and generates a segmentation mask for the object positioned at that point location. It generates pixel-level accurate segmentation and it can also deal with complex object shapes. AdaptIS is not dependent on the object bounding box proposals, and it is performing superior to those that depend on bounding boxes, even better than [24], which makes use of oriented boxes, especially for complex occluded objects. AdaptIS also outperforms \"detection first methods\" for occluded objects. Because of cost-effectiveness, real-time performance, and simplicity of vision systems set-up, in a number of vision-based robotic grasping applications, the calculation of object grasps was based on image processing of the 2D images. For example, in [25; 26] traditional image processing methods such as contour extraction and morphological operations were used. However, these methods were developed to determine optimal grasps for robotic grasping of fixed structure objects and thus were not suitable for the application considered in this work, which involves robotic grasping of very thin-stemmed plant cuttings that have irregular shapes. In [11], a combination of DL and conventional image processing was used to detect stem in images and find a grasping point so to enable automatic grasping of the stem and measurement of its diameter of maize and sorghum plants. Since sorghum bears a large and vertical stem, the presented method uses a bounding box detection rather than instance segmentation and then finds the center of the bounding box for grasp point detection. The method does not consider any occlusion and the geometry of the plant is such that there are large empty stem spaces without leaves, so leaf avoidance by the robotic gripper does not need to be considered when determining the grasps. Since the presentedmethod considers plants with a much simpler complexity than the plants considered in our intended application, it was not possible to apply its straightforward approach for sorghum to our complex plants. The authors of [27] presented a method for the calculation of grasping points to enable an automatic machine vision-guided grasping system for Phalaenopsis tissue culture plantlets. However, the presented scenario assumes an occlusion-free scene where the segmentation of individual plantlet objects is possible with a simple thresh-old. Starting from a segmented plantlet, a skeleton of the plant is extracted, and the root is distinguished from the leaves, allowing consideration of the middle point of the root as the grasping point. The skeleton of plant cuttings representing the object of interest in our application is much more complex, thus the presented approach could not be followed. This and the above examples illustrate the fact that previously published methods are applicable to specific cases of simple irregular shapes of objects, which cannot be reused for more complex objects, such as complex irregular shape plants in our application.