🌱 Plantix - AI Farming Assistant

Multi-agent AI system for crop disease diagnosis and farming guidance, built with CrewAI.

🤖 AI Agents

4 specialized agents collaborate to provide comprehensive farming solutions:

1. Disease Diagnostician 🔬

Expert plant pathologist that analyzes symptoms and environmental conditions to identify diseases with high accuracy using the Crop Disease Knowledge Database.

2. Treatment Specialist 💊

Creates immediate action plans with organic and chemical treatment options, considers budget constraints, and provides step-by-step implementation guidance.

3. Prevention Advisor 🛡️

Develops long-term prevention strategies including crop rotation, soil management, resistant varieties, and seasonal farming calendars tailored to your region.

4. Farming Consultant 🌾

Provides complete cultivation guides covering optimal growing conditions, soil prep, irrigation, fertilization schedules, harvest timing, and market strategies.

🛠️ Tools

• Crop Disease Knowledge Database - Disease info, symptoms, treatments
• Soil Analysis - Soil type recommendations and amendments
• Weather & Climate - Regional climate data for risk assessment

📋 Features

• ✅ Multi-agent collaboration for comprehensive analysis
• ✅ Context-aware recommendations based on location and climate
• ✅ Both organic and chemical treatment options
• ✅ Preventive measures and long-term strategies
• ✅ Interactive and batch processing modes
• ✅ Detailed reports saved as markdown files

🚀 Installation

1. Install dependencies:

pip install -e .

2. Set up environment variables: Create a .env file in the root directory:

GEMINI_API_KEY=your_key_here
# Or use other LLM providers supported by CrewAI

💻 Usage

Method 1: Interactive Mode (Recommended for farmers)

Run with user prompts to input crop information:

python -m agentic_ai.main

Or use the script command:

run_crew

You'll be prompted to enter:

• Crop type (e.g., Tomato, Rice, Wheat)
• Symptoms observed
• Environmental conditions
• Growth stage
• Location/region

Method 2: Pre-configured Run

Use the default run() function with example data:

agentic_ai

Method 3: Custom Script

Create your own Python script:

from agentic_ai.crew import AgenticAi

inputs = {
    'crop_type': 'Tomato',
    'symptoms': 'Yellow leaves with brown spots, wilting',
    'environment': 'High humidity, warm temperature',
    'growth_stage': 'Flowering stage',
    'location': 'Maharashtra, India'
}

result = AgenticAi().crew().kickoff(inputs=inputs)

📊 Example Input

inputs = {
    'crop_type': 'Tomato',
    'symptoms': 'Water-soaked spots on leaves, white fungal growth on undersides, rapid browning',
    'environment': 'High humidity (80%), temperature 18-22°C, recent heavy rains',
    'growth_stage': 'Flowering stage, 60 days old',
    'location': 'Maharashtra, India'
}

📄 Output

The system generates a comprehensive report (plantix_farming_report.md) containing:

1. Disease Diagnosis

   • Disease identification (common and scientific names)
   • Confidence level
   • Symptom analysis
   • Severity assessment
   • Yield impact prediction

2. Treatment Recommendations

   • Immediate action plan
   • Organic treatment options
   • Chemical treatment options (if necessary)
   • Application methods and dosages
   • Safety precautions
   • Cost estimates

3. Prevention Strategies

   • Long-term preventive measures
   • Crop rotation recommendations
   • Soil management tips
   • Disease-resistant varieties
   • Monitoring guidelines

4. General Farming Advice

   • Optimal growing conditions
   • Seasonal calendar
   • Soil and nutrient management
   • Irrigation schedule
   • Harvest and post-harvest handling

🔧 Advanced Usage

Training the Model

train <n_iterations> <filename>

Testing

test <n_iterations> <eval_llm>

Replay Previous Run

replay <task_id>

Trigger-based Execution

run_with_trigger '{"crop_type": "Rice", "symptoms": "Brown spots", ...}'

📁 Project Structure

agentic_ai/
├── src/agentic_ai/
│   ├── config/
│   │   ├── agents.yaml          # Agent configurations
│   │   └── tasks.yaml           # Task definitions
│   ├── tools/
│   │   ├── custom_tool.py       # Custom tools implementation
│   │   └── __init__.py
│   ├── crew.py                  # Crew orchestration
│   ├── main.py                  # Entry points
│   └── __init__.py
├── knowledge/
│   └── user_preference.txt      # User preferences
├── tests/
├── pyproject.toml
└── README.md

🌾 Supported Crops

The system has knowledge of major crops including:

• Vegetables: Tomato, Potato, Cucumber, Pepper, Eggplant
• Grains: Rice, Wheat, Corn
• Commercial crops: Cotton, Sugarcane
• And many more...

🐛 Common Diseases Covered

• Late Blight
• Powdery Mildew
• Bacterial Wilt
• Leaf Spots
• Root Rot
• Mosaic Viruses
• And various pest infestations

🤝 Contributing

This is a CrewAI-based project. To contribute:

1. Add new tools in tools/custom_tool.py
2. Extend agent capabilities in config/agents.yaml
3. Add new tasks in config/tasks.yaml
4. Update crew orchestration in crew.py

📝 License

This project is built using CrewAI framework.

Author

Created by TejasS1233

🙏 Acknowledgments

• Built with CrewAI
• Powered by Large Language Models
• Designed to help farmers worldwide