The Massachusetts Institute of Technology says that machine learning reveals optimal growing conditions for plants, in order to maximise taste and other features.
Botany, machine-learning algorithms, and old-fashioned chemistry make plants taste good, according to researchers in the Massachusetts Institute of Technology (MIT) Media Lab.
The researchers used computer algorithms to determine the optimal growing conditions to maximise the concentration of flavorful molecules known as volatile compounds, reports Anne Trafton of the MIT News Office.
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According to Caleb Harper, a principal research scientist in MIT’s Media Lab and director of the OpenAg group, that is just the beginning for the new field of “cyber agriculture”. 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 digitise that to allow us to understand the plant-environment interaction.”
In their study of basil plants the researchers found 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.”
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— Massachusetts Institute of Technology (MIT) (@MIT) 4 April 2019
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.
Caleb Harper, MIT:
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
“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.”
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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 maximise flavor, including the 24-hour daylight regime.
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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.
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.