Group 3

Pest Identification and Elimination Robot

Objective:

• Develop a robot that can quickly identify pests, determine their center coordinates, and eliminate them using an appropriate actuator.

Deep Learning:

1. Pest Identification:

   • Speed Requirement: Ensure the deep learning model can quickly identify pests in real-time.

   • Center Coordinates: The model should accurately determine the center coordinates of each identified pest.

   • Model Development:

     • Train the model using a dataset of various pests.

     • Focus on optimizing the model for speed and accuracy to ensure real-time performance.

2. Implementation Steps:

   • Data Collection: Collect and annotate a dataset of pests with labeled center coordinates.

   • Model Training: Train a deep learning model to recognize pests and predict their center coordinates.

   • Validation: Test the model's speed and accuracy in different environments.

Robotics:

1. Pest Elimination Actuator:

   • Requirement: Develop an actuator capable of eliminating identified pests.

   • Prototype Development:

     • Design a prototype actuator specifically for pest elimination.

     • Consider different methods such as mechanical crushing, pesticide spraying, or using electrical pulses.

2. Integration:

   • Targeting System: Ensure the robotic system can accurately target the center coordinates provided by the deep learning model.

   • Actuation: Implement the actuator in the robot to effectively eliminate the pest.

Implementation Steps:

1. Deep Learning Development:

   • Model Training: Train and optimize the deep learning model for real-time pest identification and coordinate prediction.

   • Deployment: Deploy the model onto the robot's processing unit, ensuring it can run efficiently in real-time.

2. Robotics Development:

   • Actuator Design: Design and prototype the pest elimination actuator.

   • Targeting Mechanism: Develop a mechanism to accurately target the identified coordinates of the pest.

3. System Integration:

   • Communication: Ensure seamless communication between the deep learning model and the robotic control system.

   • Automation: Implement a fully automated workflow from pest identification to elimination.

Next Steps for Students:

1. Deep Learning Tasks:

   • Dataset Collection: Collect a comprehensive dataset of pests with center coordinate annotations.

   • Model Optimization: Focus on optimizing the model for speed and accuracy.

   • Real-Time Performance: Test the model's performance in real-time scenarios.

2. Robotics Tasks:

   • Actuator Prototype: Design and build a prototype actuator for pest elimination.

   • Targeting System: Develop and test a targeting system that uses the coordinates provided by the deep learning model.

3. Testing and Iteration:

   • Prototype Testing: Test the complete system in controlled environments.

   • Iterative Improvement: Improve the system based on test results, focusing on accuracy and reliability.

Final Considerations:

• System Efficiency: Ensure the system can operate efficiently and accurately in real-time.

• Safety: Design the actuator with safety in mind to avoid unintended damage.

• Scalability: Make the system scalable for different environments and pest types.

Summary:

• Develop a fast and accurate deep learning model for pest identification and coordinate prediction.

• Design a pest elimination actuator and integrate it with the robot.

• Ensure seamless communication and automation for a fully functional pest identification and elimination system.