Open Agriculture and Artificial Intelligence for Natural Flavor
lterrat's bookmarks 2017-05-02
"But what exactly is a climate recipe for flavor, and how does a Food Computer help us optimize it? Imagine a group of expert farmers each come up with their ideal set of environmental conditions for growing very flavorful basil — light, soil, water, climate, etc. We translate those conditions into actuatable code, run that code in a Food Computer, and test and correlate the levels of flavor (in this case, volatile molecules called monoterpenes, sesquiterpenes, and phenylpropenes) generated by each plant, each time its is grown. The results from those tests inform the next round of hypotheses for what combinations of environmental conditions produce the most flavorful basil, and the process continues, evolving into an optimized climate recipe as the generations proceed, and without the limit of conventional seasons. The optimized climate recipe for flavorful basil may resemble the climate is Genoa, Italy (home of the famed Genovese Basil) or in its iterative development, it may become a climate found nowhere on Earth.
Food Computers generate roughly three-million points of data, per plant, per growth cycle — that’s a lot of data to comb through and analyze. We’re keeping this data open and accessible to the public so that anyone in the community can become part of our research, and test and improve climate recipes over time. In an ideal world, Food Computers will learn from the data as they’re generating it and the climate recipes will get better through the growth cycle.
We’ve run our first three rounds of basil experiments’ data through Sentient’s AI and we’ve already learned a few things. First, our model discovered a strong negative correlation between weight and flavor — the bigger the basil plant, the less concentrated the flavor. This is actually a well-known phenomenon in agriculture (resulting from competition between primary and secondary metabolism) but it was very encouraging to see the model pick this up so quickly.
Next, the model also discovered that flavor improvements were achieved with constant, 24-hour light periods. This surprised our team who thought basil needed a natural rest cycle. And lastly, our results showed a number of significant, nonlinear interactions between recipe variables. This confirms that optimization using surrogate modeling is a good fit for this problem. In short, the model is finding things we would never have been able to find on our own.
In the future, the methodology can be extended to optimize other outcomes, such as nutrient density or taste. It is possible to include other plants, such as cotton, to optimize fiber quality (length, strength, and fineness). It is also possible to make optimization multiobjective, and include for example, cost (kWh etc.) as well. Applications such as bioengineering, biofuels, drug design, and more, are likewise possible."