Researchers at MIT are using artificial intelligence to improve the flavor of basil, reports the MIT Technology Review. The group used AI to determine growing conditions that would increase the concentration of the volatile compounds responsible for basil’s flavor. This particular basil was grown in hydroponic units within modified shipping containers in Middleton, Massachusetts, where temperature, light, humidity, and other environmental factors were completely controlled.
The researchers gathered data by examining the taste based on specific compounds, which they determined by using gas chromatography and mass spectrometry. After feeding the results into machine-learning algorithms developed at MIT and an IT company called Cognizant, the group found that exposure to light 24-hours a day was the best recipe for the most flavorful basil.
“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,” said Caleb Harper, head of the MIT Media Lab’s OpenAg group, in a press release.
While improving the flavor of basil might seem like a fun, almost arbitrary experiment, researchers are hoping to use this technology to improve certain plants’ abilities to fight disease and study the different ways that plants may respond to the effects of climate change, which could revolutionize the agriculture industry.
Using machine learning to improve agriculture is not a concept native to MIT. Naveen Singla, who leads a data science team that studies crops at Bayer, a German multinational company that acquired Monsanto last year, says that machine learning is already being applied in some commercial farms, with flavor currently being the main target. “Flavor is one of the areas where we are heavily using machine learning—to understand the flavor of different vegetables,” he said.
“These controlled environments are where you can do a lot of optimizing by understanding the complex variables,” said Singla, explaining that machine learning has really only been able to be relevant in greenhouse growing. “In the open environments it’s still a question how we can close the gap.”
Bayer currently uses data of the plants’ genetic makeup in their algorithms, which Harper is hoping to do as their team’s next step. “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,” he said.