Hyacinth Detection System
AI-powered drone system for detecting water hyacinth in real-time
Project Overview
The Hyacinth Detection System is a Python desktop application designed to monitor and detect water hyacinth, an invasive aquatic plant species, using drone-mounted cameras and computer vision technology.
This system provides a user interface to log into the drone's camera, view a live feed, and automatically detect water hyacinth in real-time using a custom-trained computer vision model deployed through Roboflow.
The application enables environmental monitoring teams to efficiently track the spread of water hyacinth, plan removal operations, and monitor the effectiveness of control measures over time.
Technical Implementation
Computer Vision Model
Custom-trained object detection model specifically designed to identify water hyacinth in various lighting conditions and from different angles.
Roboflow Integration
Leverages Roboflow's API to stream video footage through the inference pipeline, providing real-time detection results with minimal latency.
Python Desktop Application
Built with Python and modern UI frameworks to provide an intuitive interface for drone camera control, live feed viewing, and detection visualization.
Drone Camera Integration
Secure connection and control of drone-mounted cameras, with support for various camera models and streaming protocols.
Key Features
Live Drone Feed
Real-time video streaming from drone cameras with minimal latency for immediate analysis.
AI Detection
Advanced computer vision model trained to identify water hyacinth in various conditions and environments.
Desktop Interface
User-friendly Python application for monitoring, controlling, and capturing data from the drone.
Image Capture
Capability to capture and store high-resolution images for further analysis and documentation.
Development Process
1. Model Training
Collected and annotated a dataset of water hyacinth images from various angles and conditions to train a robust detection model.
2. Roboflow Deployment
Deployed the trained model on Roboflow to enable real-time inference through their API, optimizing for speed and accuracy.
3. Desktop Application
Developed a Python application with login functionality, camera controls, and real-time detection visualization.
4. Testing & Optimization
Conducted field tests with actual drone footage to refine the model and improve the application's performance and usability.
Impact & Applications
- 1
Environmental Monitoring: Enables efficient tracking of water hyacinth spread in lakes and waterways.
- 2
Resource Optimization: Helps authorities allocate resources effectively by identifying priority areas for hyacinth removal.
- 3
Progress Tracking: Allows for before-and-after comparisons to measure the effectiveness of removal efforts.
- 4
Research Support: Provides valuable data for researchers studying invasive species spread and control methods.