Automated equipment, such as self-driving tractors, weeding robots, and data collection powered by AI, is transforming agricultural output. While these technological developments might significantly increase productivity, they also bring up new concerns regarding safety precautions and rules.
A recent study from the University of Illinois examined the most recent academic research on the security of automated agricultural devices in order to address these problems. Based on a review of more than 60 papers, the researchers identified three main topics: environmental perception, risk assessment and mitigation, and human factors and ergonomics.
“The majority of the research focuses on the first category, environmental perception. These studies primarily deal with how machines sense obstacles in the environment and respond to them. There is limited work on risk assessment or ergonomics,” said Salah Issa, Illinois Extension specialist and assistant professor in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at the U of I. Issa is corresponding author on the paper.
This is a particularly challenging issue in agriculture. In most manufacturing industries, human-robot interaction can be minimized. But some agricultural robots, such as harvesters and pickers, are designed to work in the same space as humans. The few papers on this topic explored human-robot interactions from an ergonomic perspective, focusing on how to improve machine design to ensure safety.
Salah Issa
Automated vehicles use perceptual sensors to detect things, and machine learning algorithms evaluate the data to determine when to stop, slow down, or alter course. There are three main types of obstacles that machines must be able to handle: positive, negative, and moving.
- Positive obstacles are objects that appear above ground, such as rocks, trees, and buildings.
- Negative obstacles are those that are lower than ground level, such as ditches and holes.
- Moving or dynamic obstacles are those that appear suddenly, such as a human being, an animal, or other moving machinery.
These obstacles can vary widely, depending on type of crop, features of the area, and weather conditions.
Issa and co-author Guy Roger Aby, doctoral student in ABE, found the research papers explored a wide variety of different receptor and sensor types, including 3D laser scanners, ultrasonic sensors, remote sensing, stereo vision, thermal cameras, high-resolution cameras, and more. Each type has advantages and limitations, and the most effective approaches include a combination of different methods.
“The trend in literature is towards utilizing multiple types of sensors, as opposed to just a single sensor. This is also the direction most companies are taking. It makes perfect sense for agricultural machines, given the very dynamic environments they operate in,” Issa noted.
“However, there are still many questions that need to be addressed. For example, sensors must be sensitive enough to stop immediately if a human or other object appears. But if the machine stops and the farmer is not present, would they need to go back to check on the sensor and reset the machine? This is particularly challenging when it comes to quickly moving obstacles, like a passing squirrel or bird.”
While automatic agricultural tractors and self-driving cars both face some similar obstacles, there are also some significant variations. For instance, driving in agricultural is more difficult than it is in cities, where the roads are designed and demarcated. However, erratic human behavior in other drivers is a concern on city roads, but is less of a factor in agricultural fields, Issa notes.
The second subject, methods and strategies for risk assessment, was only briefly discussed in a few research papers. Issa says this is not surprising because most systems used in engineering for risk evaluation rely on historical data. Autonomous systems in agriculture do not yet have that; there is little information that is publicly available about how they operate and what the hazards they carry.
“We believe that existing safety standards are not well-suited for autonomous systems. But there’s a significant effort underway to revise the current standards, so in a few years there will be new and revised standards,” he said. Safety regulations addressing injuries and fatalities fall under the federal Occupational Safety and Health Administration (OSHA) but some states, including California and Indiana, also have their own regulations.
The researchers identified a limited number of papers on the third topic, human factors and ergonomics.
“This is a particularly challenging issue in agriculture. In most manufacturing industries, human-robot interaction can be minimized. But some agricultural robots, such as harvesters and pickers, are designed to work in the same space as humans. The few papers on this topic explored human-robot interactions from an ergonomic perspective, focusing on how to improve machine design to ensure safety,” Issa said.
Despite being a new technology, several autonomous robots are already on the market. For instance, one business produces automatic sprayers for orchards, while autonomous tractors are being trialed and used in a few locations.
In the coming decades, automated farming equipment would surely become a necessity for contemporary farming, and Issa and Aby concluded that for their widespread adoption, reliable safety mechanisms are essential.
