AR Gardening Aid (MIT Media Lab)

AR Gardening Aid

App to make gardening easy for everyone! This work was done in then Responsive Envirorments Group at MIT Media Lab.

Problem:

Gardening is HARD!
- No one ever knows what to grow where
- Young Kids and beginners lack the intuition needed to grow plants
- Many different micro-factors affect the ability of a plant to grow
  - These factors are hard to make sense of for the average human
People have a lack of respect for nature because they struggle to understand and interact with it
Crops high in nutrition can be brought to homes and communities where they are needed at a low cost

Background: I wanted everyone from my brother to grandmother to be able to grow plants. This AR tool acts as a way to give people who have never gardened an AR assisted “green thumb.” I hope to extend this technology to create

Solution: I designed an animated digital spirit in augmented reality to assist people in indoor gardening. I systematized connections between sensors on the Arduino to Firebase. Integrated animations and visual effects in Unity to represent gathered sensor data in Firebase in real time for 20 plant types.

Possible Application:

The system was designed to be integrated into common household planters. This was choosen to allow the technology to be used by everyone. The project was also designed to be extended to larger farms.

System Design:

The system had four main components: Sensor System, Firebase Database, Unity App, and an Action Mapping. The relationship between the systems is shown below.

Hardware Design:

Whe designing the hardware the main considerations were that it was small and easy to install. I wanted to use the perfect number of sensors to get an accurate representatio of soil health, but not create too many conflicting data points.

Database Design:

The database was hosted on Firebase. The sample data hierarchy is shown below! This data would be accessed in the Unity App to trigger animations and color changes in the avatar.

Avatar Action Mapping:

Quantitative -> qualitative mapping

The data acquired by the sensors was then mapped to qualitative changes in the avatar. This was done to communicate the data in a way that could be understood by everyone!