SolarSENSE: TinyML for agriculture
Interdisciplinary
Pavan Karthik Adapa, Aditya Yatinbhai Bhatt, Ragde Chaira-Gouzounis, Sam Coltrin, Junsheng Lin, Jacob Pisors, Helen Sun, Uriah Villa, Koushik Yellumahanty
Summary
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 provides affordable, real-time soil insights to help farmers optimize resources and boost crop yields.