AI-Powered Weed Detection System
WeedWatch is a final year project that uses YOLOv11 (You Only Look Once) and Firebase to provide farmers with a simple and cost-effective way to identify weeds in real time using their mobile devices.
Although this repository does not include the full mobile app, it documents the project and its workflow, including the AI model, backend integration, and Firebase usage.
- Real-Time Weed Detection – Uses a smartphone camera with YOLOv11 to detect weeds instantly.
- AI-Powered Accuracy – Deep learning ensures fast and reliable results in different field conditions.
- Cloud Integration – Firebase handles authentication, real-time database, and cloud storage.
- Farmer-Friendly UI – The app (not included here) was designed for simplicity and ease of use.
- YOLOv11 – Object detection model for weed identification
- Python – For model training and backend scripts
- Flask – Backend API to connect model with mobile app
- Firebase – Authentication & cloud storage
- Mobile App (Frontend) – Displays detections in real time (not part of this repo)
This repo contains:
- Documentation of the system
- Sample scripts/models
- Project write-up and references
Note: The full mobile application is not included in this repository.
WeedWatch empowers farmers with an affordable AI solution for weed detection, reducing manual labor and improving crop yield. It demonstrates how AI + mobile technology can contribute to sustainable agriculture.
- Syed Shayan Haider Kazmi
- Asfand yar
- Mahad Jamnal
- Umar Abbasi
This project is for educational purposes and is part of a university final year project.