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What Smart Cities
Are Learning From Smart Farms
Cities around the world are getting smarter. Already, street lights in places like San Diego are turning off, and conserving energy, when vehicles and pedestrians aren’t around. Soon, connected garbage cans will tell waste haulers when they need to be emptied, optimizing collection routes. Smart buildings will notify maintenance staff of impending repair needs. And parking spots will find you, instead of the other way around.
Smart ideas like these aren’t confined to the city limits. They’re working in rural farm country too, helping agriculture grow more efficient and more effective every day. In fact, some of the innovations making smart cities so intelligent — like sensors, IoT connectivity, and autonomous vehicles — were raised on the farm.
Precision technology in agriculture
In the city, smart grids deliver power when and where it’s needed, based on real-time data from a network of sensors. The system monitors electricity usage, reporting shortages or outages instantly while smart relays and switches reroute power around problems automatically. It’s all designed to make the electric grid more resilient and more reliable, using less energy.
The same type of technology and resource optimization is happening in modern agriculture. Drones, satellites, and remote sensors give farmers detailed information about every corner of their operation, including soil moisture, nutrition levels, salinity, harvest data, and more. “We used to talk about farming by the foot as this radical new concept,” says Mark Young, chief technology officer at The Climate Corporation. “And now we’re pretty much farming by the seed.” The insights can be used to do things like automatically guiding variable rate application systems, including drip irrigation. Like the smart grid, variable rate irrigation delivers water on-demand, and only where it’s needed.
We used to talk about farming by the foot as this radical new concept, and now we’re pretty much farming by the seed.
CTO of The Climate Corporation
The information can also help create a digital field map of soil conditions and a custom plan for the farmer. “We’re using AI models to make a recommendation of which seeds should be planted, and where they should be planted,” Young explains. When used with GPS tractor guidance and smart implements, farmers can then apply the exact amount of nutrition, pest control, and other resources precisely when and where they’re required.
“It’s not only better for the farmer,” Young adds. “If we can be more prescriptive and help the farmer understand exactly how much the plant needs and when it’s more sustainable.”
While self-driving vehicles have just begun motoring around cities — like the 5G autonomous buses recently adopted in Zhengzhou, China — they’ve been working the farm for decades. In fact, the first autonomous tractor dates back to 1997, when a prototype created perfectly straight beds accurate to within an inch. Today, some farmers still ride in the cab, but the tractor does most of the driving.
Agricultural robots (or agbots) are also being designed for use on the farm, helping farmers grow enough and reduce crop loss while offering alternatives in a labor crunch. Automated harvesters are able to identify and pick ripe apples, strawberries, and tomatoes, all without bruising. By 2024, these robots are forecast to navigate the farm to the tune of an estimated $5.7 billion agbot industry. That’s five times the market size of 2016.
Machine learning, machine farming
The connectivity of smart cities and farms brings with it mountains of data, and the increasing ability of computers to analyze it all.
In the city, machine learning will make a big difference for your morning commute. Thousands of cameras and sensors will use pattern recognition to control traffic lights, reduce congestion and cut travel time by as much as 25%.
In agriculture, computers will process a staggering number of inputs — including visual characteristics, chemical signatures, climate variables, and thermal images, to name just a few — learning how to better care for crops. New agbot prototypes currently being field tested can autonomously navigate the farm, using machine learning to identify all plant types and remove only the weeds.
Crop photos from the same robots, as well as drones and satellites, can also be used for detecting plant stress, disease, and pest infestation. In plant breeding applications, computers can be trained to identify the expression of traits sooner than the human eye. By comparing data points from millions of photos, these systems can learn to distinguish healthy plants — and desirable characteristics — from those that are showing early signs to the contrary.
The future of agriculture
Our cities and farms might be miles apart, but they’re much closer when it comes to using the latest advancements in connectivity, automation, and machine learning. Farmers, urban planners, data scientists, engineers, and many others continue to find new ways for data to help inform better decisions while doing more with less energy and fewer resources.
The future farm will work differently, and it might look a lot different too.
Mixed sowing — or the practice of planting different crops next to each other — is difficult today. Industrial-sized harvesters can’t handle it, and traditional methods favor economies of scale. But in the future, our concept of scale might change. Miniature agbots will be able to pair the ideal crop with the exact soil conditions in a precise location, returning months later to identify and harvest individual plants.
It’s more science than science fiction to Mark Young. “We can’t manage 10,000 acres of corn the way you do your backyard garden,” he says. “But with the advent of autonomous equipment, we might.”
This diverse approach will make fields unrecognizable by today’s standards. It will make pesticide use more efficient, conserve natural resources, and improve soil health. And it will increase harvests, helping to keep smart aisles in smart grocery stores in smart cities stocked.