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Automated Agricultural Machinery Requires New Approaches to Ensuring Safety, Says Study

By Marianne Stein

From self-driving tractors to weeding robots and AI-powered data collection, automated machinery is revolutionizing agricultural production. While these technological advancements can greatly improve productivity, they also raise new questions about safety measures and regulations. To address these issues, a recent study from the University of Illinois reviewed current academic literature on the safety of automated agricultural machines. 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  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.

Automated machines detect objects through perception sensors, which are then interpreted through machine learning algorithms to direct the equipment to stop, slow down, or change direction. 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.

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