A drone hovers over Tacuarembó hospital in Uruguay, carrying its precious cargo: breast milk intended for children in remote parts of the country.
But these children need breast milk throughout their first six months, if not longer, to prevent malnutrition and other illnesses that are prevalent in rural Tacuarembó. Ground transport isn't quick or reliable enough to meet this goal. It can take several hours, during which the milk may spoil due to a lack of refrigeration.
The battery-powered drones have been the difference-maker. The project to develop them, financed by the UNICEF Innovation Fund, is the first of its kind in Latin America. To Castelli, it's nothing short of a revolution. Tacuarembó Hospital, along with three rural clinics in the most impoverished part of Uruguay, are its leaders.
"This marks the first occasion when the public health system has been directly impacted [by our technology]," says Sebastián Macías, the CEO and co-founder of Cielum, an engineer at the University Republic, which collaborated on the technology with a Uruguayan company called Cielum and a Swiss company, Rigitech.
The drone can achieve a top speed of up to 68 miles per hour, is capable of flying in light rain, and can withstand winds of up to 30 miles per hour at a maximum altitude of 120 meters.
"We have succeeded in embracing the mothers from rural areas who were previously slipping through the cracks of the system," says Ferreira, the hospital director. He envisions an expansion of the service so it can improve health for children in other rural areas.
Nurses load the drone for breast milk delivery.
Sebastián Macías - Cielum
The star aircraft
The drone, which costs approximately $70,000, was specifically designed for the transportation of biological materials. Constructed from carbon fiber, it's three meters wide, two meters long and weighs 42 pounds when fully loaded. Additionally, it is equipped with a ballistic parachute to ensure a safe descent in case the technology fails in midair. Furthermore, it can achieve a top speed of 68 miles per hour, fly in light rain, and withstand winds of 30 miles per hour at a height of 120 meters.
Inside, the drones feature three refrigerated compartments that maintain a stable temperature and adhere to the United Nations’ standards for transporting perishable products. These compartments accommodate four gallons or 6.5 pounds of cargo. According to Macías, that's more than sufficient to carry a week’s worth of milk for one infant on just two flights, or 3.3 pounds of blood samples collected in a rural clinic.
“From an energy perspective, it serves as an efficient mode of transportation and helps reduce the carbon emissions associated with using an ambulance,” said Macías. Plus, the ambulance can remain available in the town.
Macías, who has led software development for the drone, and three other technicians have been trained to operate it. They ensure that the drone stays on course, monitor weather conditions and implement emergency changes when needed. The software displays the in-flight positions of the drones in relation to other aircraft. All agricultural planes in the region receive notification about the drone's flight path, departure and arrival times, and current location.
The future: doubling the drone's reach
Forty-five days after its inaugural flight, the drone is now making five flights per week. It serves two routes: 34 miles to Curtina and 31 miles to Tambores. The drone reaches Curtina in 50 minutes while ambulances take double that time, partly due to the subpar road conditions. Pueblo Ansina, located 40 miles from the state capital, will soon be introduced as the third destination.
Overall, the drone’s schedule is expected to become much busier, with plans to accomplish 20 weekly flights by the end of October and over 30 in 2024. Given the drone’s speed, Macías is contemplating using it to transport cancer medications as well.
“When it comes to using drones to save lives, for us, the sky is not the limit," says Ciro Ferreira, Tacuarembó hospital director.
In future trips to clinics in San Gregorio de Polanco and Caraguatá, the drone will be pushed to the limit. At these locations, a battery change will be necessary, but it's worth it. The route will cover up to 10 rural Tacuarembó clinics plus one hospital outside Tacuarembó, in Rivera, close to the border with Brazil. Currently, because of a shortage of ambulances, the delivery of pasteurized breast milk to Rivera only occurs every 15 days.
“The expansion to Rivera will include 100,000 more inhabitants, doubling the healthcare reach,” said Ferreira, the director of the Tacuarembó Hospital. In itself, Ferreira's hospital serves the medical needs of 500,000 people as one of the largest in Uruguay's interior.
Alejandro Del Estal, an aeronautical engineer at Rigitech, traveled from Europe to Tacuarembó to oversee the construction of the vertiports – the defined areas that can support drones’ take-off and landing – and the first flights. He pointed out that once the flight network between hospitals and rural polyclinics is complete in Uruguay, it will rank among the five most extensive drone routes in the world for any activity, including healthcare and commercial uses.
Cielum is already working on the long-term sustainability of the project. The aim is to have more drones operating in other rural regions in the western and northern parts of the country. The company has received inquiries from Argentina and Colombia, but, as Macías pointed out, they are exercising caution when making commitments. Expansion will depend on the development of each country’s regulations for airspace use.
For Ferreira, the advantages in Uruguay are evident: "This approach enables us to bridge the geographical gap, enhance healthcare accessibility, and reduce the time required for diagnosing and treating rural inhabitants, all without the necessity of them traveling to the hospital,” he says. "When it comes to using drones to save lives, for us, the sky is not the limit."
Crickets are low on fat, high on protein, and can be farmed sustainably. They are also crunchy.
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Further reading:
More info on Bicky Nguyen
https://yseali.fulbright.edu.vn/en/faculty/bicky-n...
The environmental footprint of beef production
https://www.earthsave.org/environment.htm
https://www.watercalculator.org/news/articles/beef-king-big-water-footprints/
https://www.frontiersin.org/articles/10.3389/fsufs.2019.00005/full
https://ourworldindata.org/carbon-footprint-food-methane
Insect farming as a source of sustainable protein
https://www.insectgourmet.com/insect-farming-growing-bugs-for-protein/
https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/insect-farming
Cricket flour is taking the world by storm
https://www.cricketflours.com/
https://talk-commerce.com/blog/what-brands-use-cricket-flour-and-why/
Artificial Intelligence is already helping to grow some of the food we eat.
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.”