Building VayuRakshak: An AI-Powered Platform for Smoke Detection, Air Quality Monitoring, and Sustainable Agriculture
VayuRakshak is an integrated platform for detecting crop-residue fires, tracking potential smoke movement, and monitoring air quality using satellite data, wind information, and air-quality forecasts. It also features Kissan Sarthi, a farmer-assistance module that provides crop guidance and sustainable alternatives to stubble burning. The goal is to help citizens stay informed, authorities respond effectively, and farmers adopt cleaner practices.
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Introduction
Air pollution caused by crop-residue burning is a recurring challenge in North India. Satellite images can reveal fire hotspots, but identifying fires alone is not enough. We also need to understand where the smoke may travel, how air quality may change, and how farmers can access practical alternatives to burning crop residue.
To address this challenge, my team built VayuRakshak (वायुरक्षक), an integrated air-pollution monitoring and crop-residue management platform. It combines fire detection, smoke-path modelling, air-quality forecasting, public alerts, and AI-powered farming assistance in one application.
I led the development of the smoke-detection and air-quality monitoring systems, while my teammate developed the Kissan Sarthi farming module. We collaborated on integrating and deploying the complete application.
The problem we wanted to solve
Crop-residue burning creates pollution that can travel far beyond the location of a fire. Citizens may not know when smoke is approaching, authorities need better ways to monitor and respond to fire incidents, and farmers may lack accessible information about alternatives to burning.
VayuRakshak aims to connect these different needs through a common platform.
What does VayuRakshak do?
The platform has three main components.
1. Smoke detection and monitoring
VayuRakshak uses NASA FIRMS satellite fire detections to identify hotspots and analyse their characteristics. It estimates the likely source of fires using geographic and land-cover information, calculates estimated emissions and toxicity, and uses wind data to trace potential smoke movement.
The goal is to provide more context than a simple list of fire locations.
2. Air-quality monitoring and forecasting
The platform combines air-quality forecasts from Open-Meteo with available monitoring-station readings from OpenAQ.
Citizens can explore local air-quality information, view forecasts for the next 48 hours, understand whether smoke may reach their area, and access health guidance and alerts.
3. Kissan Sarthi: AI-powered farming assistance
Kissan Sarthi (किसान सारथी) focuses on helping farmers make informed decisions about crop health and residue management.
Its features include:
- AI-assisted crop disease guidance.
- Crop growth tracking and care recommendations.
- Estimation of emissions associated with stubble burning.
- Information about alternatives such as bio-decomposer spraying, Happy Seeders, and selling straw for biofuel.
- Farmer warnings and a no-burn pledge.
By combining agricultural assistance with smoke monitoring, the platform aims to connect pollution awareness with practical alternatives.
How the components work together
Consider a fire detected by satellite data. VayuRakshak processes the detection, estimates its characteristics, and traces a potential smoke path using wind information.
Authorities can view the incident, issue warnings, and track response cases. Citizens in potentially affected areas can check air-quality information and receive alerts. Meanwhile, farmers can explore alternatives to burning crop residue through Kissan Sarthi.
This creates a workflow that connects detection, forecasting, communication, and prevention rather than treating each as a separate problem.
Technical implementation
The backend is built with Python, while the frontend provides interactive maps and separate experiences for farmers, citizens, and authorities.
The fire-monitoring pipeline integrates external satellite, weather, and air-quality data sources. It includes fire classification based on geographic information, emissions calculations, wind interpolation, smoke trajectory estimation, and air-quality processing.
We also implemented caching to reduce repeated external API calls and avoid recalculating expensive results for every user request.
The application is designed to distinguish live data from saved demonstration samples, making its data status more transparent to users.
Challenges and lessons learned
One of our main challenges was combining several data sources with different update frequencies, formats, and limitations.
Performance and caching: Repeatedly fetching and processing external data can make an application slow. Shared caching and scheduled refreshes helped us make frequently requested results available faster.
Interpreting environmental data: A detected fire does not automatically reveal what is burning or exactly where its smoke will travel. We had to combine geographic information and wind data while accounting for uncertainty.
Building for different users: Farmers, citizens, and authorities need different information. Designing dedicated workflows helped us present the same underlying data in ways that are more relevant to each group.
Responsible AI use: AI-generated agricultural guidance can be incorrect. We treat these outputs as guidance rather than definitive diagnoses and encourage users to consult local agricultural experts for important decisions.
Limitations and future improvements
VayuRakshak is a prototype, and its predictions have important limitations. Its smoke path follows estimated wind movement rather than simulating full atmospheric dispersion. Fire-source classifications are inferred from available geographic data, and air-quality forecasts may differ from actual local conditions.
Future improvements include validating predictions against observed data, improving the accuracy of fire classification and smoke modelling, strengthening user authentication, and making alerts more reliable.
We also want to make the farming module more useful by improving agricultural recommendations and making practical alternatives to stubble burning easier to understand.
Conclusion
VayuRakshak began with a simple idea: detecting a fire is useful, but helping people understand its consequences and respond to it is even more valuable.
By combining smoke detection, air-quality monitoring, public alerts, authority workflows, and AI-assisted farming support, we aim to make environmental information more actionable for the people who need it.
Our broader goal is to support informed decisions, encourage sustainable crop-residue management, and contribute to cleaner air in North India.
VayuRakshak is a prototype. Its smoke-path estimates, fire classifications, forecasts, and AI-generated agricultural guidance should not be treated as official emergency or agricultural advice.
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