## 1 Introduction  
Toward rendering agricultural practices more targeted, efficient, profitable, and safe, robotic systems seem to be a game changer, contributing also to meet the challenges of lack of qualified labor [1, 2]. To achieve the aforementioned goals, agri-robotic applications are taking advantage of the remarkable technological advancement of information and communications technology (ICT), including artificial intelligence, sensors, and computer vision [3, 4]. Robotic solutions have found fertile ground in a number of agricultural applications, such as harvesting, spraying, disease detection, and weeding [5, 6]. Usually, robots can carry out programmed actions driven by restricted, task-specific, and noninteractive commands [7]. These prearranged actions can work sufficiently for structured environments, such as industrial stable settings. However, agriculture involves dynamic ill-defined ecosystems, which are vulnerable to heterogeneity and unforeseeable situations, as well as involve sensitive live produce [8]. As a means of addressing these challenges, the collaboration of humans and robots constitutes a promising solution through combining the cognitive human capabilities with the repeatable accuracy and strength of robots. The so-called human-robot interaction (HRI) in the agricultural sector is anticipated to provide (a) more flexibility in system reconfiguration, (b) workspace minimization, (c) optimization of both service quality and production, and (d) rapid capital depreciation [9; 10; 11]. HRI can be briefly defined as _\"the process that conveys the human operators' intention and interprets the task descriptions into a sequence of robot motions complying with the robot capabilities and the working requirements\"_[12]. The interaction of humans with computers, in general, which relies on conventional devices such as keyboards, computer mice, and remote controls, tends not to serve any more. This is mainly due to the lack of flexibility and neutrality [13; 14]. In the direction of optimizing the emerging synergistic ecosystems, more natural ways of communication are needed such as vocal interaction and body language. The former approach is particularly restricted by noisy environments and the different ways in which someone may pronounce the same command [15]. In contrast, nonverbal communication is regarded as more reliable. Body language is a concise term for summarizing body poses, facial emotions, and hand gestures. The most efficient means of natural expression that everyone is familiar with is the hand gesture interaction. A wide range of applications have taken place in HRI systems, including virtual mouse control [16], gaming technology for enhancing motor skills [17], sign language recognition [18], and human-vehicle interaction [19]. The main approaches of hand gesture recognition include the acquirement of data from either gloves equipped with sensors [20; 21] or vision sensors [15; 22]. Moreover, surface electromyography sensors have been proposed, which record the electric potential of muscles produced during their contractions [23; 24]. Summarizing the main drawbacks of the above methodologies, (a) gloves tend to limit the natural movements and may provoke adverse skin reactions in people having sensitive skins, while the variable sizes among users can cause discomfort, as stressed in [13], (b) vision sensors face difficulties in case of complex backgrounds, multiple people, and changes of illumination [15], and (c) surface electromyography usually creates massive and noisy datasets [23], which can influence the intrinsic features of classification problems [25]. Focusing on vision sensors, different algorithms and methodologies have been developed for the purpose of detecting different hand features, such as skin color [26], motion [27], skeleton [28], and depth [29], as well as their combinations.