Interactions Between Land Degradation, Drought, and People

Inspiration

The growing impact of land degradation and increasing frequency of droughts motivated us to create a solution that combines AI and environmental data to assess, predict, and mitigate the harmful effects on both nature and human well-being. We wanted to empower communities and decision-makers with accessible, real-time insights into environmental health and the impact of restoration efforts.

What it does

The system uses a network of AI agents to collect and analyze environmental data—such as weather, soil, and vegetation metrics. These agents collaborate to assess the implications of land degradation on drought risk and its effects on economic and social well-being. Additionally, it helps evaluate the effectiveness of restoration projects and provides stakeholders with timely alerts and visual insights, aiding in impactful decision-making.

How we built it

We built this project using a multi-agent system powered by uAgents. The Data Collection Agent gathers weather, soil, and vegetation data using APIs like OpenWeather and Sentinel Hub. The Impact Assessment Agent evaluates the data using AI models, while the Restoration Evaluation Agent uses collected data and drone imagery to measure the success of restoration initiatives. We integrated visualization tools like DeltaV for presenting the analysis results, and leveraged Azure Cognitive Services and Azure Maps for geospatial analysis. The entire system was built with Python, requests library for API interactions, and agent communication protocols.

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Interactions Between Land Degradation, Drought, and People

Built With

• agentverse
• fastapi
• fetchai
• python