Swarms of drones buzz overhead, while robotic vehicles plod across the landscape. Orbiting satellites capture high-resolution multi-spectral images of the vast scene below. Not a single human can be seen in the sprawling acres. Today’s agriculture is rapidly revamping into a high-tech enterprise that most 20th-century farmers could hardly recognize. It was only 100 years ago that farming transitioned from animal power to combustion engines. In the last 20 years, the global positioning system (GPS), electronic sensors among other new tools have moved farming even further into a technological wonderland. And now, robots empowered with artificial intelligence can zap weeds with extraordinary precision, while other autonomous machines move with industrious efficiency across farms.
It is no secret that the global population is expected to rise to 9.7 billion by 2050. To meet expected food demand, global agricultural output needs to increase 70%. AI is helping make that goal possible (1). It is clear a change is coming as farms are seeing an 86% decrease in labor force just in the U.S., while the number of farms continue to rise (2). While today’s agricultural technologies and AI capabilities are evolving at a rapid rate, this evolution is just beginning. Factors such as climate change, an increasing population and food security concerns have propelled the industry into seeking more innovative approaches to assure an improving crop yield.
From detecting pests to predicting which crops will deliver the best returns, artificial intelligence can help humanity oppose one of its biggest challenges: feeding an additional 2 billion people by 2050 without harming the planet.
AI is steadily emerging as an essential part of the agricultural industry’s technological evolution including self-driving machinery and flying robots that are able to automatically survey and treat crops. AI is assisting these machines in interacting together so they can begin to frame the future of fully automated agriculture. The purpose of all this high-tech gadgetry is optimization, from both economic and environmental standpoints. The goal is to only apply the optimal amount of any input (water, fertilizer, pesticide, fuel, labor) when and where it’s needed to efficiently produce high crop yields (3).
With AI bringing all components of agriculture together we can discuss how autonomous machines and drones are driving driving the future of agriculture. A future where precision robots and drones will work simultaneously to manage entire farms.
Autonomous machines can replace people performing laborious and endless tasks, such as hand-harvesting vegetables. These robots use sensor technologies, including machine vision that can detect things like the location and size of stalks/leaves to inform their mechanical processes.
In addition, the development of flying robots (drones) gives way to the possibility that most field-crop scouting currently done by humans could be replaced. Many scouting tasks, such as scouting for crop pests, require someone to walk long distances in a field, and turn over plant leaves to see the presence or absence of insects. Researchers are developing technologies to enable such flying robots to scout without human involvement. An example of this is PEAT, a Berlin-based agricultural tech startup; PEAT has developed a deep learning application called Plantix that identifies potential defects and nutrient deficiencies in plants and soil. Analysis is then conducted using machine learning and software algorithms which correlate particular foliage patterns with certain soil defects, plant pests and diseases (4). The image recognition app identifies possible defects through images captured by the user’s smartphone. Users are then provided with soil restoration techniques, tips and other potential solutions with a 95% accuracy.
Another company focused on bringing autonomous AI machinery to agriculture is Trace Genomics which focuses on machine learning for diagnosing soil defects. The California-based company provides soil analysis services to farmers. The system uses machine learning to provide clients with a sense of their soil’s strengths and weaknesses. The system attempts to prevent defective crops and maximize healthy crop production. According to the company’s website,
after submitting a sample of their soil to Trace Genomics, users receive a summary of their soils contents. Services provided in their packages range from a pathogen screening focused on bacteria and fungi to a comprehensive microbial evaluation (5).
These autonomous robots combined with drones will define the future of AI in agriculture while AI and machine learning model are helping ensure the future of crops starting from the root up.
It will take more than an army of robotic tractors to grow and harvest a successful crop. In the next 10 years, the agricultural drone industry will generate 100,000 jobs in the U.S. and $82 billion in economic activity, according to a Bank of America Merrill Lynch Global Research (6).
From spotting leaks to patrolling for pathogens, drones are taking up chores on the farm. While the presence of drones in agriculture dates back to the 1980s for crop dusting in Japan, the farms of the future will rely on machine learning models that guide the drones, satellites, and other airborne devices providing data about their crops on the ground.
As farmers try to adapt to climate change and other factors, drones promise to help make the entire farming enterprise more efficient. For instance, Descartes Labs, is employing machine learning to analyze satellite imagery to forecast soy and corn yields. The New Mexico startup collects 5 terabytes of data every day from multiple satellite constellations, including NASA and the European Space Agency (7). Combined with weather readings and other real-time inputs, Descartes Labs reports it can predict cornfield yields with high accuracy. Its AI platform can even assess crop health from infrared readings.
With the market for drones in agriculture projected to reach $480 million by 2027 (8), companies are also looking to bring drone technology to specific vertical areas of agriculture. VineView, for example, is looking to bring drones to vineyards. The company aims to help farmers improve crop yield and reduce costs (9). A farmer pre-programs a drone’s route and once deployed the drone leverages computer vision to record images which are used for later analysis.
VineView analyzes captured imagery to provide a detailed report on the health of the vineyard, specifically the condition of grapevine leaves. Since grapevine leaves are often telltales for grapevine diseases (such as molds and bacteria), reading the “health” of the leaves is often a good indicator for understanding the health of the plants and their fruit as a whole.
The company declares that its technology can scan 50 acres in 24 minutes and provides data analysis with high accuracy (10). This aerial imaging combined with AI techniques and machine learning platforms are the start of something that is being referred to as “precision agriculture”.
Precision agriculture (PA) is an approach to farm management that uses information technology to certify that crops and soil receive exactly what they need for optimum health and productivity. The goal of PA is to ensure profitability, sustainability and environmental protection. Since insecticide, for example, is only going to exactly where it is needed, environmental runoff is markedly reduced.
Precision agriculture requires three things to be successful: physical tools such as tractors and drones, site-specific information acquired by these machines, and it requires the ability to understand and make decisions based on that site-specific information.
Decision-making is often aided by AI based computer models that mathematically and statistically analyze relationships between variables like soil fertility and crop yield. Self-driving machinery and flying robots able to automatically survey and treat crops will become commonplace on farms that practice precision agriculture. Other examples of PA involve varying the rate of planting seeds in the field according to soil type and using AI analysis and sensors to identify the presence of weeds, diseases, or insects so that pesticides can be applied only where needed. The Food and Agriculture Organization of the United Nations estimates that 20 to 40 percent of global crop yields are lost each year to pests and diseases, despite the application of millions of tons of pesticides, so finding more productive and sustainable farming methods will benefit billions of people (11).
Deere & Company recently announced it would acquire a startup called Blue River Technology for a reported $305 million. Blue River has developed a “see-and-spray” system that leverages computer vision, a technology we here at Xyonix deploy regularly, to discriminate between crops and weeds. It hits the former with fertilizer and blasts the latter with herbicides with such precision that it is able to eliminate 90 percent of the chemicals used in conventional agriculture. It’s not just farmland that’s getting a helping hand from robots and artificial intelligence. A California company called Abundant Robotics, spun out of the nonprofit research institute SRI International, is developing robots capable of picking apples with vacuum-like arms that suck the fruit straight off the trees in the orchards (12). Iron Ox, out of San Francisco, is developing one-acre urban greenhouses that will be operated by robots and reportedly capable of producing the equivalent of 30 acres of farmland. Powered by artificial intelligence, a team of three robots will run the entire operation of planting, nurturing, and harvesting the crops (13). Vertical farming startup Plenty, also based in San Francisco, uses AI to automate its operations, and got a $200 million vote of confidence from the SoftBank Vision Fund earlier this year. The company claims its system uses only 1 percent of the water consumed in conventional agriculture while producing 350 times as much produce (14). Plenty is part of a new crop of urban-oriented farms, including Bowery Farming and AeroFarms.
Agricultural production has come so far in even the past couple decades that it’s hard to imagine what it will look like in a few more. But the pace of high-tech innovations in agriculture is only expanding.
Don’t be surprised if, 10 years from now, you drive down a rural highway and see small helicopters flying over a field, stopping to descend into the crop, use robotic grippers to manipulate leaves, cameras and machine vision looking for insects, and then rise back above the crop canopy and head toward its next location. All without human being in sight.
So what is in store for the future? Farmers can forecast that in the near future their drones and robots will have the AI capabilities to communicate about everything from crop assessment, counting cattle, monitoring crop diseases, water watching and mechanical pollination.