AI-powered drone system for agricultural pest detection and plant health monitoring.
Pest Patrol is a sustainability-focused project that puts machine learning in the sky. Drones fly over farmland, capture live aerial footage, and an on-board computer vision pipeline instantly classifies whether plants are healthy or under threat, giving farmers actionable insight before damage spreads.
Built at the intersection of drone engineering, computer vision, and deep learning, Pest Patrol was developed to make precision agriculture accessible and reduce crop loss through early detection.
1. Drone captures aerial footage
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2. OpenCV detects plant regions in frames
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3. ROI (Region of Interest) extracted
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4. CNN model classifies plant health
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5. Health prediction output generated
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6. Real-time visualization displayed
| Feature | Description |
|---|---|
| 🚁 Drone-Assisted Monitoring | Aerial image collection over farmland in real time |
| 👁️ Computer Vision Pipeline | OpenCV-based plant region detection |
| 🧠 CNN Classification | Deep learning model distinguishing healthy vs unhealthy crops |
| ⚡ Real-Time Analysis | Live visual data processed on the fly |
| 🌱 Sustainability Focus | Reduces pesticide overuse by targeting problem areas precisely |
- Python — core language
- OpenCV — object detection and frame processing
- TensorFlow / Keras — model training and inference
- MobileNetSSD — lightweight object detection backbone
- CNN Architecture — custom plant health classification model
- NumPy — numerical processing
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║ 🥉 3rd Place — Millennium Innovations Award ║
║ Selected among top national finalists ║
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║ 🔬 Awarded fully funded CERN research program ║
║ opportunity in Switzerland ║
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| 👤 Archil Shonia | |
| 👤 Ninia Jakhutashvili | |
| 👤 Liza Davitashvili | |
| 👤 Elene Samsiani | |
| 👤 Nita Vasadze |
Traditional pest monitoring relies on manual field inspection — slow, expensive, and often too late. Pest Patrol automates that process from above, covering more ground in minutes than a farmer could in days. By catching unhealthy crops early and precisely, it helps reduce chemical intervention, cut losses, and move agriculture toward a more sustainable future.