精东影业

At I-FARM, an 80-acre test-bed site in Urbana, Illinois, robots the size of carry-on luggage tend the fields. Amid dense stands of corn, they sow cereal rye cover crops. Under a canopy of soybeans, they measure plant traits and collect seed samples. Between rows of delicate new growth, they weed.

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Aside from its scale, this fleet of robots would appear to be just more of the same automation that large-scale producers already rely on. But in terms of data collection, data analysis, and task performance, it represents a whole new breed.

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With every data point their array of digital sensors and cameras takes in, I-FARM bots are learning about and adapting to their field environment in order to make decisions and take a variety of actions on their own鈥攏o programming necessary. Like ChatGPT and other large language models, their intelligence appears eerily human because the machine-learning algorithms animating them have trained on massive datasets. These include images rather than text, with labeled as well as unlabeled content. Occasionally they get stuck and require human intervention. But eventually their agency will be near-absolute.

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Delivering greater yields with fewer inputs

That degree of agency empowers I-FARM bots to perform two essential tasks: phenotyping and cover cropping.

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Trained to recognize top-performing plant specimens, the bots use computer vision鈥攁 system where images captured by low-cost cameras are interpreted by AI鈥攖o locate these outliers in crop rows and digitally document their traits. Actuators controlled by AI then collect their seeds. This method of phenotyping, which is accurate, efficient, and cost-effective at enormous scale, accelerates the development of high-yield crops by providing plant breeders with promising genetic material.

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Advanced computer vision also helps I-FARM bots navigate the dense, occluded environment of a standing crop. This enables farmers to sow cover crops earlier and more cost-effectively than other methods, increasing their potential for adoption. 鈥淚f smart bots made cover cropping easy and cheap, we could produce more with less鈥攚hich is what climate change demands we do,鈥� says Wedow.

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Deploying a tireless, highly skilled workforce

Such cost reductions are likely to accelerate the adoption of a digital workforce, especially when combined with AI that optimizes task performance. Already, smart bots are helping human grape harvesters by ferrying the produce they pick to collection points. Soon, as demonstrated by prototypes at AIFARMS partner institutes in California and Washington, they will be navigating rows of strawberry plants, discerning ripe berries, and plucking them without damaging the fruit or the plants. (, a robot unveiled by Root AI in 2019 can do this with grape tomatoes, but only in greenhouse environments.) Once commercialized, these smart bots could dramatically lower the price consumers pay for soft fruit like berries, tomatoes, and grapes.

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The digital workforce promises also to lower the cost of animal protein by reducing human-animal interactions鈥攊mproving both human and animal welfare in the process. Currently, farmers try to prevent costly disease outbreaks by monitoring the health of individual animals. But the larger the herd, the harder and more dangerous this job becomes. A system of cameras, microphones, and facial-recognition software from AIFARMS works in crowded barns even on muddy-faced pigs. As a result, small teams can remotely monitor thousands of animals without putting themselves at risk of injury.

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Expediting environmental resilience