In pursuit of first place in the competition, I conceived a data-driven solution designed to empower farmers with actionable insights and predictive scenarios derived from satellite imagery. The platform leveraged Artificial Intelligence, Computer Vision, and advanced analytics to translate complex satellite data into practical decision-making tools for agriculture.

The challenge focused on addressing critical agricultural needs, including irrigation optimization, wildfire prediction, soil health analysis, fertilizer application timing, and accurate weather forecasting.

To define the solution’s objectives, I conducted in-depth interviews with farmers across Brazil and collaborated with leading agricultural research institutions, including Embrapa. This discovery phase enabled the identification and mapping of the most pressing challenges in the agricultural sector.

The solution development journey progressed from UX and interface prototyping to a fully functional implementation, integrating satellite data with AI-powered models. A key milestone occurred during a visit to INPE (Instituto Nacional de Pesquisas Espaciais) in São José dos Campos, where we secured access to Brazilian satellite data.

By integrating real-time satellite information through APIs, the platform reached an unprecedented level of precision and scalability, significantly enhancing its predictive and analytical capabilities.