# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 1 Introduction  
Robotic pick-and-place systems have proven to be essential for increasing production throughput, productivity, and efficiency in numerous industrial applications [1]. The term \"robotic pick-and-place\" refers to any application in which an object is picked up by a robot at one location, moved, and placed at another location. Traditionally, four main areas of application exist for pick-and-place robots: assembly, packaging, bin-picking, and inspection. In these applications, a target object is grasped by the robot either from a conveyor belt or from a container (bin) and, depending on the application, placed on another conveyor belt, a packaging container, or at another location [2, 3]. Such robotic systems are usually equipped with vision systems used for the recognition of target objects to be grasped and moved by the robot. Recent advances in sensor and robotic gripper technology, as well as advances in artificial intelligence (AI), particularly in the area of deep learning (DL), have enabled the use of pick-and-place robots in a broad range of different industrial applications, ranging from traditional applications such as assembly of workpieces to more recent applications in the food industry and agriculture [4, 5, 6, 7]. This spread across a wide range of potential applications is characterized by the transition from traditional robotic grasping of exclusively rigid objects, mainly of a standardized size and shape, to the robotic grasping of soft, deformable, and complex-shaped objects. The latter is the case in various applications in agriculture, where complex objects with high variability and heterogeneity, such as fruits and plants, need to be manipulated by the robots. The particular challenge in such robotic applications is dealing with occludedobjects or occluded parts of objects to enable reliable robotic grasping of the target object. Some examples include fruit harvesting [8], plant phenotyping [9], and robotic plant propagation [10]. In the case of fruit harvesting, target crops are often occluded by leaves, and in robotic plant propagation applications where the plant cuttings are transported on conveyor belts, the plants tend to overlap on the conveyor belt. Therefore, vision-based detection of the target objects is a challenging task, which needs to address the problem of occlusion. Once the target object is detected, it is necessary to accurately determine its optimal grasping point so that the robotic grasping can be realized. Depending on the application and vision sensor technology employed, grasping points are calculated directly in a three-dimensional (3D) data-format using the object point cloud [9] or they are first detected in a two-dimensional (2D) single camera image using the result of object segmentation [11]. In the latter case, the 3D coordinates of the object grasping points, as needed for robotic grasping, are calculated using an approach of mapping from a 2D image to 3D working space. In this paper, novel methods are presented for the detection of target plants among the overlapped plants placed on a flat surface such as a conveyor belt, as well as for finding optimal grasps in a 2D image for possible robotic grasping of the detected target plants. Bearing in mind that considered objects of interest--plants--are flat and flexible (non-rigid) objects with complex shapes, the presented work and the achieved results represent a contribution to further progression in the field of vision-guided robotic grasping of non-rigid objects, which until now has been less researched and developed than robotic grasping of rigid objects [12]. The presented work is divided into two parts. The first presents a novel method for segmenting overlapping plants and classifying the segmented plants into different categories, aiming at the category of target plant cutting that shall be grasped by the robot. The second part presents a novel solution to the problem of finding the optimal robotic grasp of the identified target plant cutting.