Inspiration
With rising demand for exotic fruits and vegetables in urban markets and the increasing strain on farmlands, we were inspired to explore how cutting-edge technology can optimize agriculture. Farmers often struggle with uncertainty around soil quality, water availability, and climate suitability—especially for crops that aren’t native to the region. We wanted to build a system that bridges this gap by using satellite data and AI to identify where exotic crops can thrive sustainably.
What it does
Our platform analyzes satellite data (NDVI, soil moisture, temperature, rainfall patterns) and combines it with machine learning models to recommend the best regions and times for growing specific exotic crops. It provides: • Crop suitability maps for selected fruits/vegetables. • Real-time soil and weather insights. • Predictive analytics on yield and profitability. • A decision-support system for farmers and agri-startups.
How we built it • Data Collection: Pulled satellite imagery and weather data from Copernicus (Sentinel-2), NASA EarthData, and Google Earth Engine APIs. • Preprocessing: Cleaned and processed imagery using Python (GDAL, Rasterio, NumPy, Pandas). • ML Models: Trained classification and regression models (Random Forest, XGBoost, CNNs for satellite imagery) to predict crop suitability and yield. • Frontend: Built a React + TailwindCSS dashboard for visualization. • Backend: Implemented APIs using Flask/FastAPI with data pipelines in PostgreSQL + PostGIS. • Visualization: Integrated Leaflet.js / Mapbox to display heatmaps and crop recommendation layers.
Challenges we ran into • Handling massive satellite datasets and optimizing preprocessing time. • Limited availability of high-resolution soil data for certain regions. • Designing a model that balances accuracy with interpretability for farmers. • Ensuring the platform is user-friendly for non-technical stakeholders.
Accomplishments that we’re proud of • Successfully processed satellite data into usable agricultural insights. • Created an interactive dashboard that can recommend crops for a given location. • Built a working ML model that predicts crop suitability with encouraging accuracy. • Proved that technology can directly contribute to sustainable agriculture and rural development.
What we learned • The complexity of integrating geospatial data with AI in real-world scenarios. • The importance of designing solutions that are not just accurate but also usable and accessible for farmers. • How interdisciplinary innovation (AI + agriculture + geoscience) can open up new possibilities.
What’s next for Untitled • Expanding our dataset with IoT-based ground sensors to validate satellite predictions. • Partnering with agri-businesses and farmer cooperatives to pilot the system in real-world farms. • Adding market forecasting features to connect farmers with demand-side trends. • Scaling the solution into a SaaS platform that governments, startups, and farmers can adopt globally.
