Leveraging Earth Observation Data for Informed Agricultural Decision-Making
Our solution is a web platform based on remote sensing data monitoring platform. Through this platform, farmers will be able to easily access up-to-date and reliable information about their fields. Sensors installed in the fields will continuously monitor air quality and temperature values. Our users will have 24/7 access to information about the weather conditions in their agricultural areas, enabling them to make more informed and effective decisions. This innovative approach will enhance farmers' productivity. Additionally, our platform analyzes the data from the fields to identify the most suitable cropping options and presents them to users. This way, farmers can make the most effective agricultural decisions, optimizing their production and maximizing the benefits of our services.
CosTerra is an innovative web application designed to empower farmers with data-driven insights for informed agricultural decision-making. By leveraging Earth observation data, real-time sensor information, and advanced AI analysis, CosTerra provides farmers with a comprehensive understanding of their land's potential and challenges.
Our team developed CosTerra to directly address NASA's 2024 Space Apps Challenge of "Leveraging Earth Observation Data for Informed Agricultural Decision-Making." We approached this challenge by:
- Integrating satellite imagery and NDVI data to assess vegetation health and productivity.
- Incorporating real-time sensor data for up-to-date environmental information.
- Utilizing AI to generate concise, farmer-friendly land summaries.
- Creating an interactive map interface for easy location selection and data visualization.
- Implementing a chat feature for farmers to ask specific questions about their land.
CosTerra was developed using a combination of technologies:
- Frontend: Next.js, React, and MapTiler SDK for the interactive map interface.
- Backend: Python with FastAPI for processing Earth observation data and calculating NDVI statistics.
- AI Integration: OpenAI's GPT model for generating land summaries and powering the chat feature.
- Data Sources: NASA Earth observation datasets, local sensor networks, and Microsoft Planetary Computer.
We utilised NASA's Earth observation data, specifically MODIS data for long-term trend analysis of land use and crop productivity as well as NDVI index. These datasets were crucial in providing comprehensive insights into land characteristics and agricultural potential.
- Claude 3.5 Sonnet (Cursor IDE)
- GPT-4 (Spelling check & content summary generation)
For more information about our team and project, visit our Space Apps Challenge team page.