Machine
learning can reveal optimal growing conditions to maximize taste and other
features.
Anne Trafton | MIT News Office
April 3, 2019 -- What goes into making plants taste good? For
scientists in MIT’s Media Lab, it takes a combination of botany,
machine-learning algorithms, and some good old-fashioned chemistry.
Using all of the above, researchers
in the Media Lab’s Open Agriculture Initiative
report that they have created basil plants that are likely
more delicious than any you have ever tasted. No genetic modification is
involved: The researchers used computer algorithms to determine the optimal
growing conditions to maximize the concentration of flavorful molecules known
as volatile compounds.
But that is just the beginning for
the new field of “cyber agriculture,” says Caleb Harper, a principal research
scientist in MIT’s Media Lab and director of the OpenAg group. His group is now
working on enhancing the human disease-fighting properties of herbs, and they
also hope to help growers adapt to changing climates by studying how crops grow
under different conditions.
“Our goal is to design open-source
technology at the intersection of data acquisition, sensing, and machine
learning, and apply it to agricultural research in a way that hasn’t been done
before,” Harper says. “We’re really interested in building networked tools that
can take a plant’s experience, its phenotype, the set of stresses it
encounters, and its genetics, and digitize that to allow us to understand the
plant-environment interaction.”
In their study of basil plants,
which appears in the April 3 issue of PLOS ONE, the researchers found,
to their surprise, that exposing plants to light 24 hours a day generated the
best flavor. Traditional agricultural techniques would never have yielded that
insight, says John de la Parra, the research lead for the OpenAg group and an
author of the study.
“You couldn’t have discovered this
any other way. Unless you’re in Antarctica,
there isn’t a 24-hour photoperiod to test in the real world,” he says. “You had
to have artificial circumstances in order to discover that.”
Harper and Risto Miikkulainen, a
professor of computer science at the University
of Texas at Austin, are the senior authors of the paper.
Arielle Johnson, a director’s fellow at the Media Lab, and Elliot Meyerson of
Cognizant Technology Solutions are the lead authors, and Timothy Savas, a
special projects assistant at the Open Agriculture Initiative, is also an
author.
Maximizing flavor
Located in a warehouse at the
MIT-Bates Laboratory in Middleton,
Massachusetts, the OpenAg plants
are grown in shipping containers that have been retrofitted so that
environmental conditions, including light, temperature, and humidity, can be
carefully controlled.
This kind of agriculture has many
names — controlled environmental agriculture, vertical farming, urban farming —
and is still a niche market, but is growing fast, Harper says. In Japan, one such
“plant factory” produces hundreds of thousands of heads of lettuce every week.
However, there have also been many failed efforts, and there is very little
sharing of information between companies working to develop these types of
facilities.
One goal of the MIT initiative is
to overcome that kind of secrecy, by making all of the OpenAg hardware,
software, and data freely available.
“There is a big problem right now
in the agricultural space in terms of lack of publicly available data, lack of
standards in data collection, and lack of data sharing,” Harper says. “So while
machine learning and artificial intelligence and advanced algorithm design have
moved so fast, the collection of well-tagged, meaningful agricultural data is
way behind. Our tools being open-source, hopefully they will get spread faster
and create the ability to do networked science together.”
In the PLOS ONE study, the
MIT team set out to demonstrate the feasibility of their approach, which
involves growing plants under different sets of conditions in hydroponic
containers that they call “food computers.” This setup allowed them to vary the
light duration and the duration of exposure to ultraviolet light. Once the
plants were full-grown, the researchers evaluated the taste of the basil by
measuring the concentration of volatile compounds found in the leaves, using
traditional analytical chemistry techniques such as gas chromatography and mass
spectrometry. These molecules include valuable nutrients and antioxidants, so
enhancing flavor can also offer health benefits.
All of the information from the
plant experiments was then fed into machine-learning algorithms that the MIT
and Cognizant (formerly Sentient Technologies) teams developed. The algorithms
evaluated millions of possible combinations of light and UV duration, and
generated sets of conditions that would maximize flavor, including the 24-hour
daylight regime.
Moving beyond flavor, the
researchers are now working on developing basil plants with higher levels of
compounds that could help to combat diseases such as diabetes. Basil and other
plants are known to contain compounds that help control blood sugar, and in
previous work, de la Parra has shown that these compounds can be boosted by
varying environmental conditions.
The researchers are now studying
the effects of tuning other environmental variables such as temperature,
humidity, and the color of light, as well as the effects of adding plant
hormones or nutrients. In one study, they are exposing plants to chitosan, a
polymer found in insect shells, which makes the plant produce different
chemical compounds to ward off the insect attack.
They are also interested in using
their approach to increase yields of medicinal plants such as the Madagascar periwinkle,
which is the only source of the anticancer compounds vincristine and
vinblastine.
“You can see this paper as the
opening shot for many different things that can be applied, and it’s an
exhibition of the power of the tools that we’ve built so far,” de la Parra
says. “This was the archetype for what we can now do on a bigger scale.”
This approach offers an alternative
to genetic modification of crops, a technique that not everyone is comfortable
with, says Albert-László Barabási, a professor of network science at Northeastern University.
“This paper uses modern ideas in
digital agriculture to systematically alter the chemical composition of the
plants we eat by changing the environmental conditions in which the plants are
grown. It shows that we can use machine learning and well-controlled conditions
to find the sweet spots, that is, the conditions under which the plan
maximizes taste and yield,” says Barabási, who was not involved in
the study.
Climate adaptation
Another important application for cyber
agriculture, the researchers say, is adaptation to climate change. While it
usually takes years or decades to study how different conditions will affect
crops, in a controlled agricultural environment, many experiments can be done
in a short period of time.
“When you grow things in a field,
you have to rely on the weather and other factors to cooperate, and you have to
wait for the next growing season,” de la Parra says. “With systems like ours,
we can vastly increase the amount of knowledge that can be gained much more
quickly.”
The OpenAg team is currently
performing one such study on hazelnut trees for candy manufacturer Ferrero,
which consumes about 25 percent of the world’s hazelnuts.
As part of their educational
mission, the researchers have also developed small “personal food computers” —
boxes that can be used to grow plants under controlled conditions and send data
back to the MIT team. These are now used by many high school and middle school
students in the
United
States, among a network of diverse users
spread across 65 countries, who can share their ideas and results via an
online forum.
“For us, each box is a point of
data which we’re very interested in getting, but it’s also a platform of experimentation
for teaching environmental science, coding, chemistry, and math in a new way,”
Harper says.
