SolarSPELL: Offline AI for Smart Farming

Summary

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 equipment. The resulting system delivers affordable, real-time soil assessments and actionable recommendations to farmers and agricultural extension agents, supporting more resilient and sustainable farming practices.