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Scaled-Down Precision Farming Robot

Welcome to the official documentation for the Scaled-Down Precision Farming Robot. This project aims to develop an automated solution for efficient weed detection and targeting using basic image processing techniques. By leveraging the power of robotics and IoT, this robot minimizes the need for manual labor and reduces herbicide use, contributing to more sustainable farming practices.

Overview

The robot is designed to operate in controlled environments, detecting and targeting simulated weeds represented by simple patterns. It consists of two main modules:

  • Rover Module: Responsible for the robot’s movement and navigation.
  • Header Module: Houses the camera, servo motor, and laser pointer for accurate weed detection and targeting.

The system integrates various technologies:

  • MQTT: For communication between the robot and applications.
  • OpenCV: For image processing and weed detection.
  • Mobile/Desktop Applications: For user control and monitoring.

Key Features

  • Weed Detection and Targeting: The robot automatically detects weeds using image processing techniques and accurately targets them with a laser pointer.
  • Modular System Design: The robot’s components are divided into two modules—the Rover (for movement) and the Header (for weed detection and targeting)—making it easy to upgrade and modify each module independently.
  • Real-time Communication: The system uses the MQTT protocol for seamless data transfer between the robot and external applications, ensuring real-time control and monitoring.
  • Custom Applications: Users can control and monitor the robot using custom-built desktop applications (PyQt6) and mobile applications (Flutter).
  • Scalable Prototype: The robot is built with lightweight materials, allowing for easy scaling and future upgrades.
  • Sustainability: By automating the detection of weeds and reducing herbicide use, this project promotes eco-friendly farming practices.

Getting Started

To get started with the project, follow the Setup Instructions for hardware assembly, software installation, and initial testing.

Applications

This project serves as a foundation for future advancements in precision farming and eco-friendly agricultural technologies. The robot can be enhanced and scaled to include:

  • Advanced Machine Learning Algorithms: For improved weed detection accuracy and adaptability to various environments.
  • Autonomous Navigation: For full automation of the farming process.
  • Real-World Agricultural Applications: Extending the technology for use in real agricultural fields to reduce herbicide use and improve efficiency.

Contributions

This project is open-source, and we welcome contributions from developers, engineers, and researchers. Whether you’re interested in improving the existing design, adding new features, or exploring novel applications, we’d love to collaborate with you.

Check out our GitHub Repository for the full source code and further documentation. Feel free to reach out for collaboration or improvement suggestions.

Paper

For further academic insights into the development of this precision farming robot, refer to the paper on ResearchGate, which details the methodology, design, and testing process of the robot.

License

This project is licensed under the MIT License. See the LICENSE file for more information.