How computer vision increases productivity and sustainability in agriculture
Computer vision is extending the human senses. Whether it’s allowing cars to drive themselves, automating production lines for safety and efficiency, or allowing us to unlock our phones with our faces, computer vision can help make human work more productive and accurate.
For centuries, farmers have used their senses to understand and work the land to grow the crops we depend on. But today, farmers must extend their senses even further to provide the food, fuel, and fiber needed for a growing population that’s expected to exceed 10 billion by 2050, increasing global food demand by 50%. Adding to the challenge, less than 2% of the U.S. population works in agriculture, and overall employment of agricultural workers is projected to grow just by 1% from 2019 to 2029. This means farmers have less help and more work to do in the coming years.
Imagine taking care of an entire football field worth of plants — that is about an acre. Now multiply that field by about 4,000, and you’ll understand the enormous task farmers take on when planting and harvesting thousands of acres. A farm of this size can have more than 750 million plants, each experiencing microenvironments and conditions that govern their success. With so much land to keep track of, farmers need technology to help them understand what’s happening at critical junctions. That’s where computer vision comes into play.
Helping farmers see beyond the tractor cab
Computer vision helps farmers “see” crops in ways the human eye can’t. It allows farmers to manage their limited time and resources — like fertilizer, herbicides, and seeds — and gives them data and insights to make timely, accurate decisions for a healthier, more successful crop. This helps farmers be more productive, profitable, and sustainable.
The camera views the ground and enables the system to distinguish the crop plants from the weeds. | Credit: John Deere
For example, the human eye can’t distinguish a weed from a crop while driving a tractor 15 miles per hour over a field. But a sprayer equipped with computer vision can. Using cameras, onboard processors, and millions of training images, a technologically advanced sprayer can determine if a plant it sees in the field is a weed or crop and, in milliseconds, spray only on the weed with herbicide, generating significant cost savings for farmers and increasing the sustainability of their operations.
Computer vision also helps farmers see deep inside machines as they perform precise tasks, like harvesting crops at the end of a season. They use large machines that are like factories on wheels to do several things at once. For example, when harvesting corn, the machine will cut the stalk from the ground, separate the ear of corn from the stalk, and remove the individual kernels from the ear. The machine then transfers the kernels to a large cart that takes the crop from the field to storage. If one of these steps isn’t operating at peak performance, it could reduce the total crop and profits for the year. With computer vision, farmers can monitor and automatically adjust each of these crucial steps to minimize the time it takes to complete the task and ensure the best possible crop harvest in all conditions.
Enabling farmers to do more with data
In addition to helping farmers see more, computer vision also helps farmers do more. Thanks to advanced cameras, tractors can use a 360-degree view to identify objects and measure distances. Onboard processing then evaluates the images from the cameras to determine if the tractor has a clear path forward. This allows the machine to operate in the field autonomously and lets farmers essentially be in two places at once. They can set up a tractor to perform a simple, routine task in a field while they manage more complex aspects of their operations elsewhere.
Computer vision also gives farmers a constant stream of data to inform real-time decision-making. When combined with historical data from previous seasons, farmers can identify the strategies that produce the best crop and potential areas for improvement. This data takes the guesswork out of farming. Farmers only get one chance each year to produce the best possible harvest, and computer vision-enabled machines and the data they collect are key to making farming more productive to feed, fuel, and clothe our growing population.
Creating a path to a better tomorrow while feeding the world today
But it’s about more than just productivity. Farmers are stewards of the land. Their livelihood depends on it. Our planet’s health depends on it. Integrating computer vision and other advanced technologies like sensors, robotics, and AI will result in more efficient and more automated machines that can treat every single plant at a micro level, ultimately resulting in more sustainable farming operations. Computer vision is unlocking the fully autonomous farm of the future and allowing farmers to become more productive, profitable, and sustainable.
About the Author
Jorge Heraud is the vice president of automation & autonomy at John Deere, and he is passionate about combining farming and high technology. John Deere’s Automation and Autonomy group is in charge of delivering big innovations that improve our food production, such as the new Autonomous tractor that was introduced at CES in 2022 or See & Spray Ultimate, a product that uses computer vision and machine learning to reduce herbicide spray by 2/3 that was launched in 2023.
Heraud is also co-founder and CEO of Blue River Technology, a company acquired by John Deere in 2017 that specializes in computer vision, machine learning and robotics for farming.
Prior to Blue River, he worked at Trimble Navigation as business unit director for precision agriculture, and director of engineering, leading high-performing teams and creating high-technology products including automatic steering for tractors, agricultural displays, GPS receivers and variable rate controllers.
Heraud holds three Master degrees from Stanford University in Engineering and Business, as well a BS degree in Electrical Engineering from the Universidad Catolica del Peru.
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