Visual Information Requirements for Remotely Supervised Autonomous Agricultural Machines

Uduak Edet,Daniel Mann

doi:10.3390/app10082794

Abstract

A study to determine the visual requirements for a remote supervisor of an autonomous sprayer was conducted. Observation of a sprayer operator identified 9 distinct “look zones” that occupied his visual attention, with 39% of his time spent viewing the look zone ahead of the sprayer. While observation of the sprayer operator was being completed, additional GoPro cameras were used to record video of the sprayer in operation from 10 distinct perspectives (some look zones were visible from the operator’s seat, but other look zones were selected to display other regions of the sprayer that might be of interest to a sprayer operator). In a subsequent laboratory study, 29 experienced sprayer operators were recruited to view and comment on video clips selected from the video footage collected during the initial ride-along. Only the two views from the perspective of the operator’s seat were rated highly as providing important information even though participants were able to identify relevant information from all ten of the video clips. Generally, participants used the video clips to obtain information about the boom status, the location and movement of the sprayer within the field, the weather conditions (especially the wind), obstacles to be avoided, crop conditions, and field conditions. Sprayer operators with more than 15 years of experience provided more insightful descriptions of the video clips than their less experienced peers. Designers can influence which features the user will perceive by positioning the camera such that those specific features are prominent in the camera’s field of view. Overall, experienced sprayer operators preferred the concept of presenting visual information on an automation interface using live video rather than presenting that same information using some type of graphical display using icons or symbols.

Highlights

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
The participant was seen making adjustments to the sprayer settings and checking his phone. He spent 39% of his time looking at the front view (Figure 3). This finding is consistent with the results of [15], who noted that sprayer operators spent the most time looking at the field ahead, while the least attention was given to the dashboard information
Having video footage of the sprayer and its environment during spraying operation is important to the remote supervisor of an autonomous sprayer

Summary

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. 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

Methods

Results

Conclusion