## 1 Introduction
The main problem that the skin color approach involves is that the background can present elements which may match the color of the skin; therefore, different techniques have been suggested to meet this challenge [30; 31]. Concerning motion-based recognition, dynamic backgrounds or possible occlusion in front of the tracked hand can cause some issues [27; 32]. In order to meet the above challenges, depth cameras are utilized that can additionally acquire higher-resolution depth measurements, unlike RGB (red-green-blue) cameras that show only a projection. Consequently, more detailed information can be provided, since every pixel of the image consists of both color and distance values [33]. In combination with depth measurements, and in the direction of improving complex feature detection, skeleton features of the hand can be exploited. In essence, skeleton-based recognition makes use of geometric points and statistic features, including space between the joints, along with joint location, orientation, curvature, and angle [13]. A representative study is that of [PERSON] et al. [34], who classified hand gestures with the support vector machine (SVM) algorithm using a skeletal and depth dataset. Furthermore, [PERSON] et al. [35], using data from a Kinect sensor for acquisition and segmentation and the skeleton-based methodology, implemented an SVM algorithm and also a hidden Markov model (HMM) for classifying hand gestures. [PERSON] et al. [36] suggested a recursive connected component algorithm and a three-dimensional (3D) geodesic distance of the hand skeleton pixels for real-time hand gesture tracking. Lastly, deep neural networks (DNNs) were used by [PERSON] et al. [37], [PERSON] et al. [38], [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al. [41] to learn features from hand gesture images. More information about hand gesture recognition based on machine learning (ML) and computer vision can be seen in recent review studies such as [13; 42; 43; 44; 45]. As far as the incorporation of hand gesture recognition in agricultural human-robot collaborative systems is concerned, the literature is still scarce. A part of the study presented by [PERSON] et al. [46; 47] investigated this nonverbal means of communication in conjunction with a faster region-based convolutional neural network (R-CNN) intended for improving the process of avocado harvesting in a simulated workspace. In particular, in [46], a robot could detect the workers and judge if they required assistance in one of the following ways; detection of hand gestures, flag detection, and human activity recognition. Moreover, [PERSON] et al. [48] studied a virtual assembly system regarding a harvester that was able to recognize dynamic hand gestures relying on a CNN/LSTM (long short-term memory) network. Taking into account the great potential of using hand gesture recognition for enhancing the efficiency of agricultural collaborative systems [9; 10; 49], as well as for achieving fluid and safe interaction [50], the scope of the present study was to create a robust innovative paradigm. More specifically, it was aimed at developing an integrated automated communication framework between an unmanned ground vehicle (UGV) and the worker via hand gesture recognition such that the former \"collaborator\" assists humans when they require its help. Moreover, it focused on testing this synergistic ecosystem in different scenarios in an orchard. This study is an extension of the papers recently published on HRI that included a worker and a UGV for optimizing crop harvesting [51; 52; 53]. In particular, this application is based on the synchronization of the actions of the UGV and workers, with the UGV following the workers while harvesting and undertaking the transport of crates from the harvesting site to a cargo vehicle. In [51], the height of the robot deposit height was evaluated in the framework of occupational health prolepsis, while, in [52; 53], wearable sensors were used for the identification of human activity signatures.