This project explores how a chosen technology may evolve by 2036 through a month-long foresight process. It involves developing and presenting three scenario-based futures, business as usual, principled and unprincipled, through concise presentations that examine potential impacts and encourage critical thinking about how technological decisions shape outcomes.

Access to reliable soil data can be limited in off-grid agricultural settings, making it difficult to assess soil health. This project addresses that challenge by developing a soil sensing system that uses TinyML to process data locally without internet access. The system measures temperature, moisture, phosphate levels and salinity, and connects to an Android application…

Smallholder farmers in developing regions often face rapidly changing soil conditions due to climate instability, but lack access to reliable diagnostic tools. To address this challenge, the team trained a TinyML model on 100,000 soil samples from maize fields in Rwanda to interpret subtle soil variations without relying on internet connectivity, grid power, or costly…

This team applied Tiny Machine Learning, or TinyML, to enhance the SolarSENSE off-grid, solar-powered soil sensor. The system measures temperature, moisture, phosphate and salinity, with an ESP32 microcontroller processing the data locally using TinyML. Results are delivered to a smartphone via offline Wi-Fi, eliminating the need for internet access. Designed for small-scale farms, this solution…