Building a Recipe

MIT foodcomputer recipe ux

scalability & infrastructure

MIT foodcomputer recipe ux

M.I.T.’s Open Ag Food Computer

“Inside of a Food Computer, climate variables such as

  • carbon dioxide,
  • air temperature,
  • humidity,
  • dissolved oxygen,
  • potential hydrogen,
  • electrical conductivity,
  • root-zone temperature, and more can be controlled and monitored.

Usage specifications such as

  • operational energy,
  • water use,
  • and mineral consumption can also be monitored and adjusted through, electrical meters, flow sensors, and controllable chemical dosers throughout the growth period.

The complete set of conditions throughout a growth cycle can be thought of as a climate recipe, and each recipe produces unique phenotypic expressions, or physical qualities in different plants.”

MIT foodcomputer recipe

MIT foodcomputer recipe

“Plants grown under different conditions may vary in color, size, texture growth rate, yield, flavor, and nutrient density.

Food computers could be used program biotic and abiotic stresses, such as an induced drought, to create desired plant-based expressions.

It would even be possible to monitor existing natural climates and program them into downloadable recipes that could be shared around the globe.

With the creation of climate recipes, food computer users can import successful climates that have been created, tested, and perfected by other users.

The recipes can be customized and optimized for different taste or yield preferences and for various food production needs.” – Open Ag Climate Recipes – Massachusetts Institute of Technology

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food recipes tradeoff

food recipes tradeoff

“Beyond food, we are now applying this research in other industries. A system that can model human preferences and generate new ideas has many applications outside of food and the opportunity to transform customer experience.

Truly superior customer experiences are based on perception—appealing taste,

appearance and design, to name a few—and represent a major differentiator in a variety of industries, including retail, consumer goods, hospitality and travel.

As companies race to bring new products to mark computational creativity can accelerate how quickly they can bring products to market, reduce the cost of R&D, while helping them design what differentiating features should be prioritized for competitive edge.” – Cognitive Cooking Fact Sheet – IBM

Aggregate

Neural network example

data collection and recipe variables

Neural network example

Neural network example

By collecting and aggregating the automated garden’s data, a grow recipe can be tweaked and shared with a community..

A grow recipe incorporates the variables required to grow a specific plant. Some of the variables that would be included:

  • what nutrients/amendments are used
  • light cycles
  • environmental temperatures
  • root zone temperatures
  • water temperatures
  • humidity
  • C02 levels.

The Next Generation of Automated Greenhousing

US botanic gardens Washington DC 2016
US botanic gardens Washington DC 2016

US botanic gardens Washington DC 2016

By collecting and aggregating the automated garden’s data, a grow recipe can be tweaked and shared with a community.

A grow recipe incorporates the variables required to grow a specific plant. Some of the variables that would be included:

  • nutrients/amendments which are used
  • light cycles
  • environmental temperatures
  • root zone temperatures
  • water temperatures
  • humidity