AI, robotics and new automation technologies are providing welcome relief to farmers strained by rising costs, labor shortages and relentless food demands.
Nearly 1.9 million farms in the U.S., represent fertile territory for AI robotic applications ranging from weed control to self-driving tractors. Agricultural robots ranked among the top five types of professional services robots used in 2025, according to the International Federation of Robotics.
AI robotic systems handle a variety of farming tasks. Collaborative robots, or cobots, use computer vision, high-precision GPS and AI for carts that follow farm workers, carry harvested goods and navigate autonomously from point to point. And flying autonomous robots powered by AI, computer vision and machine learning algorithms harvest fruits such as apricots and apples.
Long known for its tractors and farm machinery, John Deere has been using AI automation for several years and plans to create a fully autonomous production cycle for corn and soybean farmers by 2030. In early 2022, the company launched a self-driving tractor with AI navigation using an onboard Nvidia GPU that enables the machine to detect and identify obstacles in the field.
West Bureau Farms, a family-owned operation in Princeton, Ill., uses a Deere tractor to manage land more efficiently, letting it run overnight on its own. Russell Maichel, who farms olive, prune and almond orchards in Northern California, uses the tractor to operate 24/7 and increase accuracy and productivity.
AI cuts herbicide use in half
In-the-field, Deere's See and Spray AI-powered weed-control system for large farms uses camera vision technology and machine learning to differentiate crops from weeds and target chemical spraying accordingly.
The weed-control system can cover up to 160 acres an hour, according to Deere, while AI technology quickly captures and analyzes detailed data. "It's impossible to try to preprogram software to handle every possible condition out in the world, so AI has allowed us to accelerate the development of closing all of those control loops," said Julian Sanchez, director of hydraulics, drivetrains, operator stations and quality at Deere. "You show [AI] data, and it's great at interpolating between what you show it."
Now, [the Deere system] is turning on and off just where the weeds are, and we're spraying 42% less than last year on average.
Dan AndersonPresident, Anderson Wheat Farms
In 2025, the weed control system was used on five million acres, "saved nearly 31 million gallons of spray mix and cut herbicide use by almost 50%," wrote Deere CEO and chairman John May in a LinkedIn post.
"If we were spraying a field prior to [the weed control system], we would have sprayed that field entirely with all of that chemical," said Dan Anderson, president of Anderson Wheat Farms in Haxtun, Colo., which stretches over thousands of acres. "Now, [the Deere system] is turning on and off just where the weeds are, and we're spraying 42% less than last year on average."
The automated systems and AI have provided additional information to improve farming, Anderson said. "We started moving into the data sequence of autonomy and AI, allowing us to collect that data to let us farm those acres better," he explained. "Sometimes with technology you have to make a couple of assumptions for that ROI, but most farms I know of don't just willy nilly go adopt technology. They're doing it in a step-by-step, very organized fashion, and making sure that there's a return on it every step of the way."
AI weed-killer boosts crop yield
Advancements in AI, robotics and laser technology are used on farms to detect and kill weeds. One such machine for specialty crops, called Laserweeder from Carbon Robotics, is 20 feet wide and comes equipped with lasers, computers and GPUs. It's plugged into a tractor and pulled through the field, covering up to six acres an hour, according to the company.
The machine's model was trained on more than 150 million images and understands the type of plants it's viewing as well as the evolutionary hierarchy of those plants and how they relate to each other, explained Paul Mikesell, CEO and founder of Carbon Robotics. The machine "can identify, target and shoot 10,000 weeds per minute with submillimeter accuracy," he said. "It knows on just a very simple number of pictures exactly what's going on in your field without retraining. It'll say, 'I know what that is, I know how to shoot it, how to kill it.'" Mikesell noted that eliminating weeds on farms "increases crop yield by 30% or more."
Before using the AI automated weeder, "we had to use chemicals and a lot of hand labor," said Steve Gill, owner of the fourth generation, family-owned Gills Onions farm in Oxnard, Calif., which includes 2,000 acres for growing onions and 2,000 acres for lettuce. "It saves us $500 to $1,000 an acre."
AI and robotics on farms provide improvements in efficiency, productivity and monitoring techniques.
Computer vision detects lame cows
Triple G Dairy, which has between 5,000 and 6,000 cows under management in Buckeye, Ariz., determined that fewer lame cows resulted in better milk production. So, the dairy farm installed an AI computer vision system from CattleEye, part of GEA Farm Technologies, to monitor the health of the cows.
Farmers are getting objective, consistent data on two of the most commercially significant health indicators in dairy farming, generated automatically.
Terry CanningCEO and co-founder, CattleEye
A single camera above the parlor exit monitors "every cow in the herd, at every milking, every day of the year," said Terry Canning, CEO and co-founder of CattleEye. "As each animal passes through, the system identifies her individually by body shape and coat pattern -- no tags, no wearables -- and scores her for both lameness and body condition. So, farmers are getting objective, consistent data on two of the most commercially significant health indicators in dairy farming, generated automatically, without anyone having to walk the herd or fit any additional equipment."
Since 2020, CattleEye has been collating ground truth data collected from about 100,000 cow videos with corresponding body condition scores and locomotion scores provided by veterinarians to detect lameness, Canning said. "These data points have been used to train deep learning models, which are then deployed on AWS," he added.
Most of the AI model training is done in the cloud or on the farm through an option to process the video locally via an Nvidia chipset. Early detection of lameness lets farmers treat the condition quickly before it becomes an issue. When undetected, lameness could cost as much a $450 per cow per year in milk production, according to CattleEye.
Chuck Martin, a New York Times bestselling author, futurist, speaker and columnist, has been a thought leader in emerging digital technologies for more than three decades.
