Group 4

Farming Robot for Plant Inspection and Treatment

Objective:

• Develop a farming robot that can autonomously move to specific fields, take photos of plants, inspect for illnesses, and apply the appropriate treatment.

Deep Learning:

1. Plant Inspection:

   • Requirement: Inspect plants one by one, identifying specific illnesses.

   • Plants and Illnesses:

     • Plants: Three kinds of plants.

     • Illnesses: Two specific illnesses for each plant.

   • Model Development:

     • Train deep learning models to recognize each type of plant and its associated illnesses.

     • Ensure the model can distinguish between healthy and unhealthy plants.

2. Treatment Identification:

   • Objective: The model should also determine the appropriate treatment (medication) for identified illnesses.

   • Implementation:

     • Develop a system to link identified illnesses to specific treatments.

     • Integrate this information so the robot can inform the robotics side about the necessary action.

Robotics:

1. Autonomous Movement:

   • Navigation: Ensure the robot can autonomously navigate to specific fields.

   • Localization: Implement a reliable localization system to accurately position the robot within the field.

2. Photo Capture:

   • Camera System: Equip the robot with a camera to take high-quality photos of plants.

   • Data Transmission: Ensure the robot can send these photos to a central system for deep learning analysis.

3. Actuation and Treatment Application:

   • Actuators: Equip the robot with actuators capable of performing various actions.

   • Actions:

     • Watering: Implement a system to water plants as needed.

     • Nutrient Spraying: Equip the robot with a mechanism to spray nutrients.

     • Pesticide Application: Develop a system to spray pesticides based on identified illnesses.

Implementation Steps:

1. Deep Learning Development:

   • Model Training: Train models to identify plants and specific illnesses.

   • Treatment Mapping: Create a database linking illnesses to treatments.

2. Robotics Development:

   • Navigation System: Develop and test the autonomous navigation system.

   • Actuation Mechanisms: Design and implement actuators for watering, nutrient spraying, and pesticide application.

3. Integration:

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

   • Automation: Implement a fully automated workflow from plant inspection to treatment application.

Next Steps for Students:

1. Deep Learning Tasks:

   • Collect and annotate a dataset for the three kinds of plants and their illnesses.

   • Train and validate the deep learning models for accurate plant and illness identification.

   • Develop the treatment recommendation system.

2. Robotics Tasks:

   • Design and build the robot with necessary actuators and camera system.

   • Develop the navigation system for autonomous movement.

   • Integrate the actuation mechanisms for applying treatments.

3. Testing and Iteration:

   • Test the complete system in a controlled environment.

   • Iterate on the design based on test results to improve accuracy and reliability.

Final Considerations:

• Data Collection: Ensure a robust dataset for deep learning model training.

• System Reliability: Focus on creating a reliable and durable robot that can operate in various field conditions.

• Scalability: Design the system to be scalable for different field sizes and plant types.

Summary:

• Develop deep learning models to inspect plants and identify specific illnesses.

• Equip the robot with actuators for applying treatments such as watering, nutrient spraying, and pesticide application.

• Ensure seamless integration between the deep learning system and the robotic control system for fully automated plant inspection and treatment.