Autonomous, indoor farming gives a boost to crops
The glass-encased cabinet looks like a display meant to hold reasonably priced watches, or drugstore beauty creams shipped from France. But instead of this stagnant merchandise, each of its five shelves is overgrown with leaves — moss-soft pea sprouts, spikes of Lolla rosa lettuces, pale bok choy, dark kale, purple basil or red-veined sorrel or green wisps of dill. The glass structure isn’t a cabinet, but rather a “micro farm.”
The gadget is on display at the Richmond, Virginia headquarters of Babylon Micro-Farms, a company that aims to make indoor farming in the U.S. more accessible and sustainable. Babylon’s soilless hydroponic growing system, which feeds plants via nutrient-enriched water, allows chefs on cruise ships, cafeterias and elsewhere to provide home-grown produce to patrons, just seconds after it’s harvested. Currently, there are over 200 functioning systems, either sold or leased to customers, and more of them are on the way.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
The Nose Knows: Dogs Are Being Trained to Detect the Coronavirus
Asher is eccentric and inquisitive. He loves an audience, likes keeping busy, and howls to be let through doors. He is a six-year-old working Cocker Spaniel, who, with five other furry colleagues, has now been trained to sniff body odor samples from humans to detect COVID-19 infections.
As the Delta variant and other new versions of the SARS-CoV-2 virus emerge, public health agencies are once again recommending masking while employers contemplate mandatory vaccination. While PCR tests remain the "gold standard" of COVID-19 tests, they can take hours to flag infections. To accelerate the process, scientists are turning to a new testing tool: sniffer dogs.
At the London School of Hygiene and Tropical Medicine (LSHTM), researchers deployed Asher and five other trained dogs to test sock samples from 200 asymptomatic, infected individuals and 200 healthy individuals. In May, they published the findings of the yearlong study in a preprint, concluding that dogs could identify COVID-19 infections with a high degree of accuracy – they could correctly identify a COVID-positive sample up to 94% of the time and a negative sample up to 92% of the time. The paper has yet to be peer-reviewed.
"Dogs can screen lots of people very quickly – 300 people per dog per hour. This means they could be used in places like airports or public venues like stadiums and maybe even workplaces," says James Logan, who heads the Department of Disease Control at LSHTM, adding that canines can also detect variants of SARS-CoV-2. "We included samples from two variants and the dogs could still detect them."
Detection dogs have been one of the most reliable biosensors for identifying the odor of human disease. According to Gemma Butlin, a spokesperson of Medical Detection Dogs, the UK-based charity that trained canines for the LSHTM study, the olfactory capabilities of dogs have been deployed to detect malaria, Parkinson's disease, different types of cancers, as well as pseudomonas, a type of bacteria known to cause infections in blood, lungs, eyes, and other parts of the human body.
COVID-19 has a distinctive smell — a result of chemicals known as volatile organic compounds released by infected body cells, which give off an odor "fingerprint."
"It's estimated that the percentage of a dog's brain devoted to analyzing odors is 40 times larger than that of a human," says Butlin. "Humans have around 5 million scent receptors dedicated to smell. Dogs have 350 million and can detect odors at parts per trillion. To put this into context, a dog can detect a teaspoon of sugar in a million gallons of water: two Olympic-sized pools full."
According to LSHTM scientists, COVID-19 has a distinctive smell — a result of chemicals known as volatile organic compounds released by infected body cells, which give off an odor "fingerprint." Other studies, too, have revealed that the SARS-CoV-2 virus has a distinct olfactory signature, detectable in the urine, saliva, and sweat of infected individuals. Humans can't smell the disease in these fluids, but dogs can.
"Our research shows that the smell associated with COVID-19 is at least partly due to small and volatile chemicals that are produced by the virus growing in the body or the immune response to the virus or both," said Steve Lindsay, a public health entomologist at Durham University, whose team collaborated with LSHTM for the study. He added, "There is also a further possibility that dogs can actually smell the virus, which is incredible given how small viruses are."
In April this year, researchers from the University of Pennsylvania and collaborators published a similar study in the scientific journal PLOS One, revealing that detection dogs could successfully discriminate between urine samples of infected and uninfected individuals. The accuracy rate of canines in this study was 96%. Similarly, last December, French scientists found that dogs were 76-100% effective at identifying individuals with COVID-19 when presented with sweat samples.
Grandjean Dominique, a professor at France's National Veterinary School of Alfort, who led the French study, said that the researchers used two types of dogs — search and rescue dogs, as they can sniff sweat, and explosive detection dogs, because they're often used at airports to find bomb ingredients. Dogs may very well be as good as PCR tests, said Dominique, but the goal, he added, is not to replace these tests with canines.
In France, the government gave the green light to train hundreds of disease detection dogs and deploy them in airports. "They will act as mass pre-test, and only people who are positive will undergo a PCR test to check their level of infection and the kind of variant," says Dominique. He thinks the dogs will be able to decrease the amount of PCR testing and potentially save money.
Since the accuracy rate for bio-detection dogs is fairly high, scientists think they could prove to be a quick diagnosis and mass screening tool, especially at ports, airports, train stations, stadiums, and public gatherings. Countries like Finland, Thailand, UAE, Italy, Chile, India, Australia, Pakistan, Saudi Arabia, Switzerland, and Mexico are already training and deploying canines for COVID-19 detection. The dogs are trained to sniff the area around a person, and if they find the odor of COVID-19 they will sit or stand back from an individual as a signal that they've identified an infection.
While bio-detection dogs seem promising for cheap, large-volume screening, many of the studies that have been performed to date have been small and in controlled environments. The big question is whether this approach work on people in crowded airports, not just samples of shirts and socks in a lab.
"The next step is 'real world' testing where they [canines] are placed in airports to screen people and see how they perform," says Anna Durbin, professor of international health at the John Hopkins Bloomberg School of Public Health. "Testing in real airports with lots of passengers and competing scents will need to be done."
According to Butlin of Medical Detection Dogs, scalability could be a challenge. However, scientists don't intend to have a dog in every waiting room, detecting COVID-19 or other diseases, she said.
"Dogs are the most reliable bio sensors on the planet and they have proven time and time again that they can detect diseases as accurately, if not more so, than current technological diagnostics," said Butlin. "We are learning from them all the time and what their noses know will one day enable the creation an 'E-nose' that does the same job – imagine a day when your mobile phone can tell you that you are unwell."
The Voice Behind Some of Your Favorite Cartoon Characters Helped Create the Artificial Heart
In June, a team of surgeons at Duke University Hospital implanted the latest model of an artificial heart in a 39-year-old man with severe heart failure, a condition in which the heart doesn't pump properly. The man's mechanical heart, made by French company Carmat, is a new generation artificial heart and the first of its kind to be transplanted in the United States. It connects to a portable external power supply and is designed to keep the patient alive until a replacement organ becomes available.
Many patients die while waiting for a heart transplant, but artificial hearts can bridge the gap. Though not a permanent solution for heart failure, artificial hearts have saved countless lives since their first implantation in 1982.
What might surprise you is that the origin of the artificial heart dates back decades before, when an inventive television actor teamed up with a famous doctor to design and patent the first such device.
A man of many talents
Paul Winchell was an entertainer in the 1950s and 60s, rising to fame as a ventriloquist and guest-starring as an actor on programs like "The Ed Sullivan Show" and "Perry Mason." When children's animation boomed in the 1960s, Winchell made a name for himself as a voice actor on shows like "The Smurfs," "Winnie the Pooh," and "The Jetsons." He eventually became famous for originating the voices of Tigger from "Winnie the Pooh" and Gargamel from "The Smurfs," among many others.
But Winchell wasn't just an entertainer: He also had a quiet passion for science and medicine. Between television gigs, Winchell busied himself working as a medical hypnotist and acupuncturist, treating the same Hollywood stars he performed alongside. When he wasn't doing that, Winchell threw himself into engineering and design, building not only the ventriloquism dummies he used on his television appearances but a host of products he'd dreamed up himself. Winchell spent hours tinkering with his own inventions, such as a set of battery-powered gloves and something called a "flameless lighter." Over the course of his life, Winchell designed and patented more than 30 of these products – mostly novelties, but also serious medical devices, such as a portable blood plasma defroster.
Ventriloquist Paul Winchell with Jerry Mahoney, his dummy, in 1951 |
A meeting of the minds
In the early 1950s, Winchell appeared on a variety show called the "Arthur Murray Dance Party" and faced off in a dance competition with the legendary Ricardo Montalban (Winchell won). At a cast party for the show later that same night, Winchell met Dr. Henry Heimlich – the same doctor who would later become famous for inventing the Heimlich maneuver, who was married to Murray's daughter. The two hit it off immediately, bonding over their shared interest in medicine. Before long, Heimlich invited Winchell to come observe him in the operating room at the hospital where he worked. Winchell jumped at the opportunity, and not long after he became a frequent guest in Heimlich's surgical theatre, fascinated by the mechanics of the human body.
One day while Winchell was observing at the hospital, he witnessed a patient die on the operating table after undergoing open-heart surgery. He was suddenly struck with an idea: If there was some way doctors could keep blood pumping temporarily throughout the body during surgery, patients who underwent risky operations like open-heart surgery might have a better chance of survival. Winchell rushed to Heimlich with the idea – and Heimlich agreed to advise Winchell and look over any design drafts he came up with. So Winchell went to work.
Winchell's heart
As it turned out, building ventriloquism dummies wasn't that different from building an artificial heart, Winchell noted later in his autobiography – the shifting valves and chambers of the mechanical heart were similar to the moving eyes and opening mouths of his puppets. After each design, Winchell would go back to Heimlich and the two would confer, making adjustments along the way to.
By 1956, Winchell had perfected his design: The "heart" consisted of a bag that could be placed inside the human body, connected to a battery-powered motor outside of the body. The motor enabled the bag to pump blood throughout the body, similar to a real human heart. Winchell received a patent for the design in 1963.
At the time, Winchell never quite got the credit he deserved. Years later, researchers at the University of Utah, working on their own artificial heart, came across Winchell's patent and got in touch with Winchell to compare notes. Winchell ended up donating his patent to the team, which included Dr. Richard Jarvik. Jarvik expanded on Winchell's design and created the Jarvik-7 – the world's first artificial heart to be successfully implanted in a human being in 1982.
The Jarvik-7 has since been replaced with newer, more efficient models made up of different synthetic materials, allowing patients to live for longer stretches without the heart clogging or breaking down. With each new generation of hearts, heart failure patients have been able to live relatively normal lives for longer periods of time and with fewer complications than before – and it never would have been possible without the unsung genius of a puppeteer and his love of science.