## 1 Introduction  
The era of autonomous agricultural machines (AAMs) seems to be just around the corner. Engineers and researchers are working diligently to design AAMs that will enable farmers to increase the productivity of their operations. When contemplating the incorporation of automation into an existing machine, [1] presented a model that can be used by design engineers to identify which functions should be automated. Their model is based on the four-stage model of human information processing and proposes that automation can be applied to four distinct types of functions: information acquisition, information analysis, decision and action selection, and action implementation. The engineer's task is to assign the appropriate level of automation (ranging from manual to fully autonomous) for each of the four distinct types of functions. An AAM is likely to have a high level of automation for each of these independent types of functions. Despite the fact that an AAM is designed to automate information acquisition and analysis (using various sensors), to automate decision-making (through well-designed programming or machine learning approaches), and to automate action implementation (through various actuators linked to on-board sensors), it is imperative that design engineers do not forget to consider human interaction with the automation. Although it might be tempting to think of AAMs working in the field as independent of any human interaction, it is prudent to consider a larger system that is composed of the AAM working autonomously in the field and the human who will be monitoring its operation from a location that is remote from the field [2; 3; 4]. From this larger system perspective, the human supervisor, who is interested in both the status and progress of the AAM, will remotely supervie the AAM through some type of display, which can be aptly described as an \"automation interface\". It has been observed that humans face difficulty in maintaining a high level of awareness of system states when the system is under the control of either another human or of automation [5]. This decline in situation awareness is problematic for the human responsible for supervising the AAM; the authors of [6] advised that special attention must be given to the design of the automation interface to minimize such reduction in situation awareness. The ultimate productivity of such a human-machine system will depend on the type of information that is presented to the supervisor through an automation interface and the ability of the supervisor to efficiently and effectively glean the information from the interface. Therefore, great importance must be placed on the design of the automation interface so that the entire human automation system can be optimized. Previous studies [7; 8; 9] have suggested that video footage is essential to supervisors when monitoring a machine remotely. The authors of [10] proposed a system architecture to enable control of an autonomous tractor that included a \"coordinating process\", which was to be handled by a coordinator located remotely in a farm office. They proposed the phrase \"tractor mimic display\" for the computer interface that would be used by the coordinator to obtain tractor status information [10], also suggesting that the mimic display would incorporate a \"real-time video link to steerable on-board cameras\" to allow the coordinator to have \"a better understanding of the tractor's environment.\" Several other research groups have described similar concepts, where a remote supervisor has some level of control over one or more AAMs [7; 11; 12]. The authors of [10] stated that the inclusion of video will enable the supervisor to better understand the tractor's environment, while the authors of [13] reported that a large majority of participants identified the presence of live video as being important for understanding machine functions. Furthermore, participants were more secure in their interpretation of visual information if live video was present [13].