Drone Development & Data Classification
Led a government-funded multidisciplinary project to design and build an affordable open-source quadcopter for agricultural data analysis using Machine Learning. [Patent Pending]
Project Overview
From July 2022 to February 2023, I led a government-funded project for the Ministry of Science and Technology, India, focused on developing an affordable open-source quadcopter solution for agricultural data collection and analysis. This multidisciplinary initiative combined hardware engineering, software development, and machine learning to create a practical tool for modern farming applications.
The project is currently Patent Pending and represents a significant contribution to accessible agricultural technology in India.
Government Partnership
This project was funded by the Government of India's Ministry of Science and Technology as part of their initiative to promote indigenous agricultural technology development. Working with a multidisciplinary team of engineers, agronomists, and data scientists, we created a solution that addresses the real-world needs of Indian farmers.
The goal was to democratize access to precision agriculture tools by creating an affordable, locally maintainable alternative to expensive imported drone solutions.
Hardware Design & Assembly
As the lead hardware engineer, I spearheaded the design and assembly of an affordable open-source quadcopter optimized for agricultural use. The design prioritized durability, ease of repair, and local availability of components.
Key Design Features
- Modular frame design for easy maintenance and repairs
- Flight time optimized for agricultural survey missions (25-30 minutes)
- Weather-resistant construction for field conditions
- Payload capacity for various sensors and cameras
- GPS-enabled autonomous flight capabilities
- Open-source flight controller for customization
Cost Optimization
Through careful component selection and local sourcing, we achieved a final unit cost that was 70% lower than comparable imported solutions, making the technology accessible to small and medium-scale farmers.
The use of open-source hardware and software ensures no licensing fees and allows for community-driven improvements.
Machine Learning Integration
The drone system integrates advanced machine learning capabilities for automated agricultural data analysis. The ML pipeline processes aerial imagery to extract actionable insights for farmers.
Data Classification
- Crop health assessment using NDVI analysis
- Pest and disease detection through image recognition
- Irrigation need identification via soil moisture mapping
- Yield prediction based on growth patterns
- Weed detection and mapping for targeted treatment
ML Model Training
Trained custom computer vision models on Indian agricultural datasets, ensuring accuracy for local crop varieties and conditions. The models achieved 92% accuracy in crop health classification and 88% in pest detection.
Web Application Development
Developed a comprehensive web application that serves as the control center and data visualization platform for the drone system. The application provides an intuitive interface for farmers and agricultural specialists to plan missions, view real-time data, and access analytical reports.
Core Features
- Interactive map interface for flight planning
- Real-time telemetry and video streaming
- Automated mission scheduling and waypoint navigation
- Historical data viewing and comparison
- Multi-language support (Hindi, English, regional languages)
- Mobile-responsive design for field use
Data Analytics Dashboard
Built comprehensive dashboards displaying processed ML results with visualizations including heat maps, growth trends, and actionable recommendations. Reports can be exported in multiple formats for sharing with agronomists or government agencies.
Technical Stack
Hardware Components
- Custom quadcopter frame (open-source design)
- Pixhawk flight controller
- GPS module with RTK capability
- Multispectral camera for crop analysis
- LiDAR sensor for terrain mapping
- Telemetry radio for ground station communication
Software Technologies
- Python for ML model development (TensorFlow, OpenCV)
- React for web application frontend
- Node.js backend with Express
- PostgreSQL with PostGIS for spatial data
- ROS (Robot Operating System) for drone communication
- Docker for deployment and scaling
Field Testing & Results
Conducted extensive field testing across multiple farms in Uttar Pradesh and Madhya Pradesh, collecting data from wheat, rice, and sugarcane fields. The system successfully operated in varying weather conditions and terrain types, demonstrating its practical viability.
Feedback from pilot farmers indicated significant time savings in field assessment (reduced from days to hours) and improved crop yield through early problem detection. The ML-powered analysis helped farmers optimize irrigation and pesticide application, resulting in resource savings of up to 30%.
Project Impact & Future Work
This project demonstrates the potential of combining affordable hardware with advanced software to create accessible agricultural technology solutions. The open-source nature of the design enables other researchers and organizations to build upon this work.
With the patent pending, we are exploring opportunities to scale the solution through government agricultural programs and farmer cooperatives. The project has been recognized by the Ministry of Science and Technology as a model for indigenous technology development in precision agriculture.