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Food. What we eat, and how we grow it, will be fundamentally transformed in the next decade.
Already, indoor farming is projected to be a US$40.25 billion industry by 2022, with a compound annual growth rate of 9.65 percent. Meanwhile, the food 3D printing industry is expected to grow at an even higher rate, averaging 50 percent annual growth.
And converging exponential technologies—from materials science to AI-driven digital agriculture—are not slowing down. Today’s breakthroughs will soon allow our planet to boost its food production by nearly 70 percent, using a fraction of the real estate and resources, to feed 9 billion by mid-century.
What you consume, how it was grown, and how it will end up in your stomach will all ride the wave of converging exponentials, revolutionizing the most basic of human needs.
3D printing has already had a profound impact on the manufacturing sector. We are now able to print in hundreds of different materials, making anything from toys to houses to organs. However, we are finally seeing the emergence of 3D printers that can print food itself.
Redefine Meat, an Israeli startup, wants to tackle industrial meat production using 3D printers that can generate meat, no animals required. The printer takes in fat, water, and three different plant protein sources, using these ingredients to print a meat fiber matrix with trapped fat and water, thus mimicking the texture and flavor of real meat.
Slated for release in 2020 at a cost of $100,000, their machines are rapidly demonetizing and will begin by targeting clients in industrial-scale meat production.
Anrich3D aims to take this process a step further, 3D printing meals that are customized to your medical records, heath data from your smart wearables, and patterns detected by your sleep trackers. The company plans to use multiple extruders for multi-material printing, allowing them to dispense each ingredient precisely for nutritionally optimized meals. Currently in an R&D phase at the Nanyang Technological University in Singapore, the company hopes to have its first taste tests in 2020.
These are only a few of the many 3D food printing startups springing into existence. The benefits from such innovations are boundless.
Not only will food 3D printing grant consumers control over the ingredients and mixtures they consume, but it is already beginning to enable new innovations in flavor itself, democratizing far healthier meal options in newly customizable cuisine categories.
Vertical farming, whereby food is grown in vertical stacks (in skyscrapers and buildings rather than outside in fields), marks a classic case of converging exponential technologies. Over just the past decade, the technology has surged from a handful of early-stage pilots to a full-grown industry.
Today, the average American meal travels 1,500-2,500 miles to get to your plate. As summed up by Worldwatch Institute researcher Brian Halweil, “We are spending far more energy to get food to the table than the energy we get from eating the food.” Additionally, the longer foods are out of the soil, the less nutritious they become, losing on average 45 percent of their nutrition before being consumed.
Yet beyond cutting down on time and transportation losses, vertical farming eliminates a whole host of issues in food production. Relying on hydroponics and aeroponics, vertical farms allows us to grow crops with 90 percent less water than traditional agriculture—which is critical for our increasingly thirsty planet.
Currently, the largest player around is Bay Area-based Plenty Inc. With over $200 million in funding from Softbank, Plenty is taking a smart tech approach to indoor agriculture. Plants grow on 20-foot-high towers, monitored by tens of thousands of cameras and sensors, optimized by big data and machine learning.
This allows the company to pack 40 plants in the space previously occupied by 1. The process also produces yields 350 times greater than outdoor farmland, using less than 1 percent as much water.
And rather than bespoke veggies for the wealthy few, Plenty’s processes allow them to knock 20-35 percent off the costs of traditional grocery stores. To date, Plenty has their home base in South San Francisco, a 100,000 square-foot farm in Kent, Washington, an indoor farm in the United Arab Emirates, and recently started construction on over 300 farms in China.
Another major player is New Jersey-based Aerofarms, which can now grow two million pounds of leafy greens without sunlight or soil.
To do this, Aerofarms leverages AI-controlled LEDs to provide optimized wavelengths of light for each plant. Using aeroponics, the company delivers nutrients by misting them directly onto the plants’ roots—no soil required. Rather, plants are suspended in a growth mesh fabric made from recycled water bottles. And here too, sensors, cameras, and machine learning govern the entire process.
While 50-80 percent of the cost of vertical farming is human labor, autonomous robotics promises to solve that problem. Enter contenders like Iron Ox, a firm that has developed the Angus robot, capable of moving around plant-growing containers.
The writing is on the wall, and traditional agriculture is fast being turned on its head.
In an era where materials science, nanotechnology, and biotechnology are rapidly becoming the same field of study, key advances are enabling us to create healthier, more nutritious, more efficient, and longer-lasting food.
For starters, we are now able to boost the photosynthetic abilities of plants. Using novel techniques to improve a micro-step in the photosynthesis process chain, researchers at UCLA were able to boost tobacco crop yield by 14-20 percent. Meanwhile, the RIPE Project, backed by Bill Gates and run out of the University of Illinois, has matched and improved those numbers.
And to top things off, The University of Essex was even able to improve tobacco yield by 27-47 percent by increasing the levels of protein involved in photo-respiration.
In yet another win for food-related materials science, Santa Barbara-based Apeel Sciences is further tackling the vexing challenge of food waste. Now approaching commercialization, Apeel uses lipids and glycerolipids found in the peels, seeds, and pulps of all fruits and vegetables to create “cutin”—the fatty substance that composes the skin of fruits and prevents them from rapidly spoiling by trapping moisture.
By then spraying fruits with this generated substance, Apeel can preserve foods 60 percent longer using an odorless, tasteless, colorless organic substance.
And stores across the US are already using this method. By leveraging our advancing knowledge of plants and chemistry, materials science is allowing us to produce more food with far longer-lasting freshness and more nutritious value than ever before.
With advances in 3D printing, vertical farming, and materials sciences, we can now make food smarter, more productive, and far more resilient.
By the end of the next decade, you should be able to 3D print a fusion cuisine dish from the comfort of your home, using ingredients harvested from vertical farms, with nutritional value optimized by AI and materials science. However, even this picture doesn’t account for all the rapid changes underway in the food industry.
Join me next week for Part 2 of the Future of Food for a discussion on how food production will be transformed, quite literally, from the bottom up.
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Image Credit: Vanessa Bates Ramirez Continue reading
What’s the world’s hardest machine learning problem? Autonomous vehicles? Robots that can walk? Cancer detection?
Nope, says Julian Sanchez. It’s agriculture.
Sanchez might be a little biased. He is the director of precision agriculture for John Deere, and is in charge of adding intelligence to traditional farm vehicles. But he does have a little perspective, having spent time working on software for both medical devices and air traffic control systems.
I met with Sanchez and Alexey Rostapshov, head of digital innovation at John Deere Labs, at the organization’s San Francisco offices last month. Labs launched in 2017 to take advantage of the area’s tech expertise, both to apply machine learning to in-house agricultural problems and to work with partners to build technologies that play nicely with Deere’s big green machines. Deere’s neighbors in San Francisco’s tech-heavy South of Market are LinkedIn, Salesforce, and Planet Labs, which puts it in a good position for recruiting.
“We’ve literally had folks knock on the door and say, ‘What are you doing here?’” says Rostapshov, and some return to drop off resumes.
Here’s why Sanchez believes agriculture is such a big challenge for artificial intelligence.
“It’s not just about driving tractors around,” he says, although autonomous driving technologies are part of the mix. (John Deere is doing a lot of work with precision GPS to improve autonomous driving, for example, and allow tractors to plan their own routes around fields.)
But more complex than the driving problem, says Sanchez, are the classification problems.
Corn: A Classic Classification Problem
Photo: Tekla Perry
One key effort, Sanchez says, are AI systems “that allow me to tell whether grain being harvested is good quality or low quality and to make automatic adjustment systems for the harvester.” The company is already selling an early version of this image analysis technology. But the many differences between grain types, and grains grown under different conditions, make this task a tough one for machine learning.
“Take corn,” Sanchez says. “Let’s say we are building a deep learning algorithm to detect this corn. And we take lots of pictures of kernels to give it. Say we pick those kernels in central Illinois. But, one mile over, the farmer planted a slightly different hybrid which has slightly different coloration of yellow. Meanwhile, this other farm harvested three days later in a field five miles away; it’s the same hybrid, but it also looks different.
“It’s an overwhelming classification challenge, and that’s just for corn. But you are not only doing it for corn, you have to add 20 more varieties of grain to the mix; and some, like canola, are almost microscopic.”
Even the ground conditions vary dramatically—far more than road conditions, Sanchez points out.
“Let’s say we are building a deep learning algorithm to detect how much residue is left on the soil after a harvest, including stubble and some chaff. Let’s drive 2,000 acres of fields in the Midwest looking at residue. That’s great, but I guarantee that if you go drive those the next year, it will look significantly different.
“Deep learning is great at interpolating conditions between what it knows; it is not good at extrapolating to situations it hasn’t seen. And in agriculture, you always feel that there is a set of conditions that you haven’t yet classified.”
A Flood of Big Data
The scale of the data is also daunting, Rostapshov points out. “We are one of the largest users of cloud computing services in the world,” he says. “We are gathering 5 to 15 million measurements per second from 130,000 connected machines globally. We have over 150 million acres in our databases, using petabytes and petabytes [of storage]. We process more data than Twitter does.”
Much of this information is so-called dirty data, that is, it doesn’t share the same format or structure, because it’s coming not only from a wide variety of John Deere machines, but also includes data from some 100 other companies that have access to the platform, including weather information, aerial imagery, and soil analyses.
As a result, says Sanchez, Deere has had to make “tremendous investments in back-end data cleanup.”
Deep learning is great at interpolating conditions between what it knows; it is not good at extrapolating to situations it hasn’t seen.”
—Julian Sanchez, John Deere
“We have gotten progressively more skilled at that problem,” he says. “We started simply by cleaning up our own data. You’d think it would be nice and neat, since it’s coming from our own machines, but there is a wide variety of different models and different years. Then we started geospatially tagging the agronomic data—the information about where you are applying herbicides and fertilizer and the like—coming in from our vehicles. When we started bringing in other data, from drones, say, we were already good at cleaning it up.”
John Deere’s Hiring Pitch
Hard problems can be a good thing to have for a company looking to hire machine learning engineers.
“Our opening line to potential recruits,” Sanchez says, “is ‘This stuff matters.’ Then, if we get a chance to talk to them more, we follow up with ‘Not only does this stuff matter, but the problems are really hard and interesting.’ When we explain the variability in farming and how we have to apply all the latest tools to these problems, we get their attention.”
Software engineers “know that feeding a growing population is a massive problem and are excited about the prospect of making a difference,” Rostapshov says.
Only 20 engineers work in the San Francisco labs right now, and that’s on a busy day—some of the researchers spend part of their time at Blue River Technology, a startup based in Sunnyvale that was acquired by Deere in 2017. About half of the researchers are focusing on AI. The Lab is in the process of doubling its office space (no word on staffing plans for that expansion yet).
“We are one of the largest users of cloud computing services in the world.”
—Alexey Rostapshov, John Deere Labs
Company-wide, Deere has thousands of software engineers, with many using AI and machine learning tools in their work, and about the same number of mechanical and electrical engineers, Sanchez reports. “If you look at our hiring 10 years ago,” he says, “it was heavily weighted to mechanical engineers. But if you look at those numbers now, it is by a large majority [engineers working] in the software space. We still need mechanical engineers—we do build green machines—but if you go by our footprint of tech talent, it is pretty safe to call John Deere a software company. And if you follow the key conversations that are happening in the company right now, 95 percent of them are software-related.”
For now, these software engineers are focused on developing technologies that allow farmers to “do more with less,” Sanchez says. Meaning, to get more and better crops from less fuel, less seed, less fertilizer, less pesticide, and fewer workers, and putting together building blocks that, he says, could eventually lead to fully autonomous farm vehicles. The data Deere collects today, for the most part, stays in silos (the virtual kind), with AI algorithms that analyze specific sets of data to provide guidance to individual farmers. At some point, however, with tools to anonymize data and buy-in from farmers, aggregating data could provide some powerful insights.
“We are not asking farmers for that yet,” Sanchez says. “We are not doing aggregation to look for patterns. We are focused on offering technology that allows an individual farmer to use less, on positioning ourselves to be in a neutral spot. We are not about selling you more seed or more fertilizer. So we are building up a good trust level. In the long term, we can have conversations about doing more with deep learning.” Continue reading