The Agricultural Technology Gap
In 2022, I joined a government-funded project at IIIT-L with an ambitious goal: democratize agricultural technology for Indian farmers. The problem was clear - commercial agricultural drones cost ₹5-10 lakhs ($6,000-$12,000), putting them out of reach for small and medium-sized farms.
Our mission: build an affordable, open-source quad-copter with machine learning capabilities that could:
- Capture high-resolution aerial imagery
- Analyze crop health in real-time
- Detect pest infestations and diseases early
- Generate actionable insights for farmers
All for under ₹50,000 ($600).
The Multidisciplinary Challenge
This wasn't just a software project - it required expertise in hardware design, embedded systems, computer vision, and machine learning. I led a team of 12 students across these disciplines. My role spanned project management, ML implementation, and systems integration.
Hardware Design Philosophy
Open-Source First
We designed every component to be:
- 3D-printable or locally sourceable
- Modifiable for different use cases
- Well-documented for replication
Machine Learning Pipeline
Dataset Building
We collected over 50,000 images across 3 crop types (wheat, rice, mustard) and 4 health states:
- Healthy
- Water-stressed
- Nutrient-deficient
- Disease/pest-affected
Each image was manually labeled by agricultural experts from IIIT-L's partner organizations.
Model Architecture
For on-device inference, we needed a lightweight model. I chose MobileNetV2 as the base, achieving:
- Validation Accuracy: 87.3%
- Model Size: 9.2 MB (quantized to 2.4 MB for edge deployment)
- Inference Time: 180ms on Raspberry Pi 4
Field Testing Results
We conducted trials on 5 farms across Uttar Pradesh with overwhelmingly positive results. Early detection of water stress, nutrient deficiency, and pest infestations saved estimated 20-30% yield loss.
Open-Source Impact
We released everything on GitHub under MIT license:
- Hardware design files (STL, CAD)
- Flight control firmware
- ML training code and datasets
- Web application source
The project has since been:
- Replicated by 15+ universities for research
- Used by 3 agricultural startups as foundation for commercial products
- Featured in agricultural technology conferences
- Downloaded 2,000+ times
Lessons Learned
1. Talk to Your Users Early
We almost built the wrong thing. Early farmer interviews revealed they cared more about actionable insights than raw data.
2. Reliability > Features
A drone that works 95% of the time isn't good enough. We spent months on failure recovery and error handling.
3. Documentation is Critical
For open-source hardware, clear documentation is the difference between "interesting project" and "replicated impact."
This project reinforced my belief that technology should be accessible to everyone, not just those who can afford expensive commercial solutions. Open-source hardware and machine learning have the potential to transform agriculture in developing countries.