The Signal: Two tracks are running at once. In Asia and Africa, $60 million from the OpenAI Foundation is pushing AI weather advice to 100 million smallholders through governments. In the United States, farm drones and autonomous machines are already working — and the rules and the policy are still catching up. Watch which track sets the standard.
The money moved toward scale this week. The OpenAI Foundation put $60 million behind AI weather and crop disease forecasts, with a goal of 100 million smallholder farms in three years. A new CAST brief says farm AI is already working while federal policy lags behind. Canada is funding computer vision for vineyards, Danish vegetables are being weeded by a robot, and Tellia raised $5 million so field crews can talk instead of type. You’ll find the stories below that mattered most.
— Warren
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Notre Dame’s AIM for Scale won a $10 million grant from the OpenAI Foundation. The money will help governments send AI weather and crop disease forecasts to farmers in South and Southeast Asia and East Africa. The wider program totals $60 million. Partners include the University of Chicago, UC Berkeley, Precision Development, Digital Green and CIMMYT. The goal is 100 million smallholder farms in three years.
Why it matters: Smallholder farms grow about a third of the world’s food. Most still farm without local forecasts. Cheap, accurate forecasts help them plant on time and lose less to drought and disease.
AI Angle: AI weather models need far less computing power than older forecast systems. Advice reaches farmers by text, phone call and chatbot.
Danish grower Axel Månsson has run an AgXeed T2 7-SERIES robot tractor with a Carbon Robotics LaserWeeder since April. The two machines talk to each other directly, and travel speed drops when weeds get thick. Månsson runs one of Scandinavia’s largest organic vegetable farms from Jutland, Denmark. He gives the setup 4.5 stars on most measures of performance.
Why it matters: Organic vegetable farms lean on hand weeding, which is costly and hard to staff, so two machines that coordinate can cut those hours.
AI Angle: The laser weeder uses cameras to find weeds and burns them with light. No herbicide is involved, and the robot adjusts its own speed to the job.
A new CAST brief says AI is already at work on US farms, while federal policy lags behind. The report is peer-reviewed. The lead author is Alex Thomasson of Mississippi State University. The brief says AI is no longer limited to research labs. It is now built into farm equipment and food processing.
Why it matters: On farms, machines see conditions and reason about them. They act, with people supervising. The brief says policy decides how fast this goes. Connectivity and workforce training shape who benefits. Data quality and trust matter too.
AI Angle: The authors describe a shift from analyzing data to acting on it. See & Spray is one example. Laser weeding is another.
Norway’s NoFence launched the N3 collar and a livestock management platform. The solar-powered collars hold cattle, sheep and goats behind a virtual fence. NoFence reports a 99.7% containment rate. The software writes grazing reports for each paddock, schedules moves and tracks rumination to flag breeding windows. US ranchers are the company’s fastest-growing market.
Why it matters: Virtual fencing cuts the time spent moving fences and driving out to check cattle. That time goes back into the business.
AI Angle: Collars combine GPS, a collar-to-collar mesh network and satellite backup, so herds stay visible where cell coverage fails.
Tellia raised $5 million in pre-seed funding led by Revent, a European venture firm. The startup is based in San Francisco and Paris. It turns phone calls, voice notes and WhatsApp messages into structured farm records. Each note lands on the right field, crop, crew and job. Crews can also ask operational questions and get answers back.
Why it matters: Tellia says 80% of field teams at its US sites use it daily, and that figure comes within two months of onboarding. Customers include almond grower Campos Brothers Farms and Duckhorn wineries. Field observations usually stay in notebooks, group chats or memory. Voice capture saves them without asking crews to stop work and type.
AI Angle: The product is a voice layer, not another platform to learn. Tellia is building an API so other agtech tools can embed it.
Canada will invest up to $1.69 million in machine learning and computer vision for grape growers. Vivid Machines of Toronto will adapt imaging tools it built for apple orchards. Cameras scan the vines and check for disease. They also judge fruit quality and estimate the crop load. The money comes through the AgriScience Program. That federal fund pays for farm innovation.
Why it matters: Finding disease early can save a season’s crop. Better yield estimates help growers plan harvest crews, storage and sales.
AI Angle: Machine learning reads the images and surfaces patterns that field scouting can miss.
Rules for farm drones keep changing, and Hylio says buyers should ask where the hardware was built. The FCC added foreign-made drones and drones with foreign parts to its Covered List in December 2025. Operators may need several licenses before they can spray. Hylio, based in Richmond, Texas, builds as much of its equipment at home as it can.
Why it matters: Input costs are up 50% to 60% since 2019, says CEO Arthur Erickson. Targeted spraying can stretch a smaller chemical budget across the same acres.
AI Angle: The pitch is precision. Sensors and software treat only the affected zone, so less chemical does the same work.
