Inside a shipping container-sized box at MIT Media Lab, crops of basil are growing in micro-climates designed by artificial intelligence. The first experiments, with controlled levels and duration of UV light, aim to grow a tastier version. As the mini-greenhouse generates roughly 3 million data points for each growth cycle of a single plant, the AI uses machine learning to analyze it and create new and better “climate recipes”–which can then be shared with anyone trying to grow food indoors.
Researchers at the Media Lab first developed a prototype of what they call the OpenAg Personal Food Computer in 2015. The contained growing environment, packed with sensors, actuators, and machine vision, can study and then replicate optimal growing conditions for food, changing everything from the pattern and spectrum of light used to the salinity of water and the nutrients added. A larger version, the Food Server, is the size of a shipping container, with racks of plants that can each be grown with unique variables. Initially, the researchers analyzed the data themselves to improve their climate recipes. But in June 2016, the team began working with the AI company Sentient to use its software to optimize the growing environment more quickly.
The GLP aggregated and excerpted this blog/article to reflect the diversity of news, opinion, and analysis. Read full, original post: These Food Computers Use AI To Make “Climate Recipes” For The Best-Tasting Crops