The big picture: Alphabet's X and John Deere, startups and universities are looking to AI-based agriculture to address these problems. But farming presents hard problems for AI that, if solved, could ultimately help it be deployed in more structured places (think: homes).
Machine learning is used to analyze data collected from farmers' fields, satellites and drones and inform decisions about planting and fertilizing, to spot disease, and to try to predict crop yields.
The big field test for AI, though, is whether it can abandon following a script and be trained to adapt to a dirty, messy and uncertain life on the farm. "If you can deploy it in an unstructured environment, it will work in a more structured one," says Mueller-Sim.
Data: Success in computer vision has largely come from deep learning, an AI technique that relies on data with detailed labels and tags. "The challenge is we don't necessarily have that supervised data for ag," Chowdhary says.
Uncertainty: Robots struggle with change. Soil texture, glare, clouds and other variables can all interfere with movement and computer vision, particularly as it tries to move towards driverless tech.
Variation: Another challenge for AI in agriculture — and more broadly — is developing it to be accurate within and across different fields.
Adoption: Farmers often rely on generations of knowledge about their land. So, new — and often expensive — technologies will have to prove their worth.
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