AI, robotics, and new automation technologies are providing welcome relief to farmers squeezed by rising costs, labor shortages, and unrelenting demand for food.
The approximately 1.9 million farms in the U.S. are a hotspot for applications of AI robots, from weed control to self-driving tractors. According to the International Federation of Robotics, agricultural robots ranked among the top five types of professional service robots to be used in 2025.
AI robotic systems handle various agricultural tasks. Collaborative robots (cobots) use computer vision, high-precision GPS, and AI to cart, track farm workers, carry crops, and move autonomously from point to point. Then, flying autonomous robots powered by AI, computer vision, and machine learning algorithms will harvest fruits such as apricots and apples.
John Deere, long known for its tractors and farm equipment, has been leveraging AI automation for several years and plans to create fully autonomous production cycles for corn and soybean farmers by 2030. In early 2022, the company launched a self-driving tractor with AI navigation using onboard Nvidia GPUs, allowing the machine to detect and identify obstacles in the field.
West Bureau Farms, a family-owned business in Princeton, Illinois, uses Deere tractors to manage their land more efficiently, allowing them to drive themselves overnight. Russell Machel, who runs an olive, prune, and almond orchard in Northern California, runs his tractors 24/7 to improve accuracy and productivity.
AI cuts herbicide usage in half
In the field, Deere’s See and Spray AI-powered weed control system for large-scale farms uses camera vision technology and machine learning to distinguish between crops and weeds and direct chemical sprays accordingly.
Deere said the weed control system can cover up to 160 acres per hour, and AI technology quickly captures and analyzes detailed data. “You can’t try to pre-program software to handle every situation that can happen in the world, so AI has allowed us to accelerate development to close all of these control loops,” said Julian Sanchez, Deere’s director of hydraulics, drivetrain, operator stations and quality. “You show [AI] It’s good at transforming data and interpolating between what’s displayed. ”
now, [the Deere system] We are turning it on and off where weeds are growing, and on average we are spraying 42% less than last year.
Dan AndersonAnderson Wheat Farm President
In 2025, the weed control system will be used on 5 million acres of land, saving “approximately 31 million gallons of spray mixture and reducing herbicide use by nearly 50%,” Deere CEO and Chairman John May wrote in a LinkedIn post.
“If we had sprayed the fields before, [the weed control system]”If we had sprayed all those chemicals all over that field,” said Dan Anderson, president of Anderson Wheat Farms, a multi-thousand acre farm in Haxtan, Colorado. [the Deere system] On average, the amount of spraying is 42% lower than last year because we only turn on and off where weeds are growing. ”
Automated systems and AI have provided additional information to improve agriculture, Anderson said. “We started moving into autonomy and AI data sequencing so we could collect that data and farm those acres better,” he explained. “With technology, you may have to make some assumptions about ROI, but most farms I know don’t adopt technology aggressively. They do it in stages and in a very methodical way, making sure they get a benefit at each stage.”
AI herbicides increase crop yields
Advances in AI, robotics, and laser technology are being used to detect and kill weeds on farms. One such machine for specialty crops, Carbon Robotics’ Laserweeder, is 20 feet wide and equipped with a laser, computer, and GPU. The company says it connects to a tractor and pulls fields, covering up to six acres in an hour.
The machine’s model has been trained on more than 150 million images to understand the types of plants it’s displaying, the evolutionary hierarchy of those plants, and how they relate to each other, explained Paul Mikesell, CEO and founder of Carbon Robotics. He said the machine “can identify and target 10,000 weeds per minute with sub-millimeter accuracy.” “With just a very simple number of pictures, you know exactly what’s going on in the field without any retraining. You say, ‘I know what it is, I know how to shoot it, I know how to kill it.'” Mikesell said eliminating weeds on a farm “increases crop yields by more than 30 percent.”
Before using AI automated weeders, “we had to use chemicals and a lot of manual labor,” says Steve Gill, fourth-generation owner of the family-owned Gill’s Onions Farm in Oxnard, California. The farm includes 2,000 acres for growing onions and 2,000 acres for growing lettuce. “You can save $500 to $1,000 per acre.”
AI and robotics on farms improve efficiency, productivity, and monitoring techniques.
Computer vision detects lame cow
Triple G Dairy, which manages 5,000 to 6,000 cows in Buckeye, Arizona, has determined that fewer lame cows means better milk production. To monitor cow health, the dairy farm installed an AI computer vision system from CattleEye, part of GEA Farm Technologies.
Farmers now automatically generate and receive objective, consistent data on two of the most commercially important health indicators in dairy farming.
terry canningCEO and Co-Founder of CattleEye
Terry Canning, CEO and co-founder of Cattle Eye, said a camera above the parlor exit “monitors every cow in the herd every day of the year, at every milking.” “As each animal passes, the system identifies individuals by body shape and coat pattern and scores them for both lameness and body condition, without the use of tags or wearable devices. So farmers have objective, consistent data on two of the most commercially important health indicators in dairy farming that are generated automatically, without anyone walking the herd or wearing any additional equipment.”
Since 2020, CattleEye has been matching ground truth data collected from videos of approximately 100,000 cows with corresponding body condition and locomotor scores provided by veterinarians to detect lameness, Canning said. “These data points are used to train deep learning models, which are then deployed to AWS,” he added.
Most of the training for AI models is done in the cloud or on the farm, with the option to process video locally via Nvidia chipsets. Early detection of lameness allows farmers to quickly treat lameness before it becomes a problem. Undetected lameness can cost milk production as much as $450 per cow per year, according to Cattle Eye.
chuck martin new york times A bestselling author, futurist, speaker, and columnist, he has been a thought leader in emerging digital technologies for over 30 years.
