This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: You've likely heard this assumption: When AI-enabled and autonomous machines arrive on the farm, jobs will disappear. But researchers at the University of California, Davis, who studied 16 fruit tree operations in California and Washington for three years, found that automation still takes human knowledge.
What changes is how the work gets done. The study, drawing on 165 in-depth interviews with workers and growers, was published in the journal New Technology, Work and Employment. The study examined what researchers call the "last mile" of agricultural automation: what happens after robotic and AI-enabled tools arrive on the farm and have to work in real conditions that can change by the hour.
"Automation isn't something that just happens to a farm or happens to a labor force," said lead author M. Anne Visser, professor of community and regional development in the UC Davis Department of Human Ecology. "People have to actively make it happen." Every orchard is different, so technology must be adapted to each one.
That often requires human knowledge about the land, the fruit or even the expected weather. No matter how good these machines get, Visser said, they may not be able to match that embedded place-based knowledge. For example, a tractor or ATV can automatically travel down orchard rows and scan trees with a digital camera that counts buds and fruit to help forecast yield.
The technology mimics a farmworker physically counting buds on every sixth or seventh tree to estimate yield. But researchers found the technology tended to overestimate yield and often required interpretation through historical knowledge of the field. One farm manager with over a decade on the farm adjusted the camera's counts down by 14% because of historical understanding of block performance and recent weather events.
His estimates ended up more accurate than the cameras'. Even with tools in place, people are still needed to interpret what the technology is seeing, adapt it to field conditions, troubleshoot problems and bridge the gap between how a tool was designed to work and how farming actually works. Autonomous harvesters are another example.
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