The word ‘agriculture’ connotes certain images: workers toiling under the sun, vast fields of crops, tractors, and plows. It’s a world apart from shiny Silicon Valley, but technology has always been as integral to agriculture as any other sector, where new equipment and techniques can maximize efficiency, safety, and crop yield. Now gleaming computers and dusty fields are merging – and potentially changing agriculture as we know it.
A team of researchers from MIT Media Lab’s Open Agriculture Initiative published a paper detailing a recent study where machine learning algorithms were used to optimize growing conditions for basil plants (with an eye on more expansive future use cases). The burgeoning field – called “cyber agriculture” by the scientists – combines a variety of disciplines to grow the most flavorful, disease-resistant crops possible in a given environment.
“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,” says Open Agriculture director and MIT Media Lab principal research scientist Caleb Harper. “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.”
Open Agriculture performs the studies in its warehouse headquarters at MIT-Bates Laboratory. Inside are specially-retrofitted shipping containers, designed “so that environmental conditions, including light, temperature, and humidity, can be carefully controlled.” The ability to artificially control conditions in these “food computers” has already yielded unexpected insight – the researchers’ initial study of basil plants revealed the plants had the best flavor after being exposed to round-the-clock light. “You couldn’t have discovered this any other way,” said John de la Parra, one of several co-authors of the study and an OpenAg research lead.
The burgeoning field of cyber agriculture has long suffered from siloed information, making MIT’s commitment to open-source tools especially significant. Equally so is their use of machine-learning algorithms, developed in conjunction with Cognizant, that “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.”
Researchers have now set their sights on “developing basil plants with higher levels of compounds that could help to combat diseases such as diabetes” without any genetic modification while continuing to explore the effects of different environmental features. De la Parra calls the paper “the archetype” for future large-scale applications of the tools, as well as an opportunity to study climate change adaptation in far quicker, controlled ways than in nature. More insight will surely be gleaned from further study, but initial returns are clear: the future of agriculture may very well mix analog and digital realms for the best results.
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