Welcome to Segma-Vision Agriculture Documentation !

Table of contents:

• Team
  • Supervisor :
  • Members :
• Segma-Vision
  • Overview
  • How It Works
  • What’s Unique About Segma-Vision?
  • Project Building Strategy
  • Applications
  • Future Directions
  • Conclusion
• Segma-Vision Agriculture
  • Overview
  • Concept of Using Multiple Agents
  • Pipeline
  • Conclusion
• Input Agent
  • Overview
  • How It Works
  • Task Decision Flow Diagram
  • Task Decision Code
  • Task Examples
  • Code for Input Agent
• Coordinator Agent
  • Key Functions
  • Code Implementation
  • Next Steps:
• Task-Specific Agents
  • Overview of Task-Specific Agents
  • Architecture
  • Detailed Presentation of Task-Specific Agents
  • Integration in the System
• Plant Disease Detection Agent
  • Overview of Plant Disease Detection
  • Functionality
  • Use Case
  • Architecture
  • Fine-Tuning the Model
  • Colab Notebook for Fine-Tuning
  • Integration with Segma-Vision
  • Code Overview
• Soil Classification Agent
  • Overview
  • How It Works
  • Model Architecture
  • Hyperparameters
  • Training Process
  • Training and Validation Accuracy Graph
  • Model Evaluation
  • Task Decision Code
  • Task Examples
  • Conclusion
• Yield Prediction Agent
  • Overview
  • Agent Pipeline
  • Training Process
  • Training Diagram
  • Prediction Workflow
  • Model Performance Metrics
  • Best Model: Random Forest
  • Weather Data Fetching and Crop Yield Prediction