Autonomous Modular Agriculture Robot

Overview: This project introduces an autonomous, modular agricultural robot designed to enhance farming efficiency through precision agriculture. The robot integrates multiple interchangeable modules to perform various farming tasks, reducing the need for individual robots for separate tasks. This system leverages ROS, Computer Visison, SolidWorks, Ansys.

Key Features:

• Modular Design: Reduces manufacturing and assembly costs by allowing task-specific module attachments.
• AI & Computer Vision Module: Weed detection & classification for precision pesticide spraying.
• Autonomous & Manual Operation: Uses ROS framework for real-time navigation and decision-making.
• Integrated Sensors & Simulation: Camera, GPS and Ultlrasonic sensors for perception and movement control.

Results & Impact

AI-powered detection models achieved 86% accuracy in weed and disease classification. Differential drive control enabled smooth robot navigation in Gazebo simulations. Cost-effective modular design reduced manufacturing costs.

Future Work:

• Improve AI models for higher accuracy in weed & disease detection.
• Expand modular capabilities to support more farming tasks.
• Enhance autonomous navigation** using advanced path planning algorithms.

Technologies Used:

• Robot Control: ROS, Gazebo, RViz
• AI & Computer Vision: TinyVGG (CNN), Deep Learning for crop & weed detection
• Navigation: Differential drive, Lane detection, Sensor fusion
• IoT & User Interface: Android app for remote monitoring and control

This multifunctional robotic platform represents the future of smart farming, offering scalability, cost-effectiveness, and high efficiency for modern agricultural needs.