🌾 FarmIQ: Intelligent Crop Yield & Agentic Advisory System

FarmIQ is a professional-grade agricultural intelligence platform that combines Machine Learning (ML) for precise yield forecasting with Agentic AI to provide actionable farming advisory reports.

🌟 Key Features

• Precision Yield Forecasting: Ensemble ML models (Random Forest & Linear Regression) predict crop yield based on environmental factors.
• Agentic AI Advisory: A specialized LLM agent analyzes predictions and identifies risk factors (drought, thermal stress, nutrient gaps).
• Professional UI/UX: A modern, agricultural-themed dashboard built with Streamlit, featuring glassmorphism and intuitive card-based inputs.
• PDF Report Export: Generate and download comprehensive agronomy reports in PDF format for offline record-keeping.
• Risk Mitigation: Automated identification of environmental hazards with specific recommended actions.

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🏗️ System Architecture

graph TD
    A[User Input Portal] --> B[Data Preprocessing]
    B --> C{ML Inference Engine}
    C -->|Random Forest| D[Yield Prediction]
    C -->|Linear Regression| E[Model Metrics]
    D --> F[Agentic AI Layer]
    F -->|Risk Analysis| G[LLM Reasoning]
    G --> H[Advisory Report]
    H --> I[PDF Export Engine]
    I --> J[Downloadable Report]

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🔄 Platform Workflow

To get the most out of FarmIQ, follow this logical flow:

1. Data Entry: Provide details about your crop (type, season), local weather (rainfall, temp), soil nutrients (N, P, K), and farm management (irrigation, area).
2. Analysis: Click "Analyze" to trigger the ML engine and AI reasoning agent.
3. Review Results:
   • Observe the Predicted Yield (tons/ha) and its productivity level (High/Medium/Low).
   • Examine the Driving Environmental Factors chart to understand what impacted your yield.
   • Read the Strategic Agro-Advisory sections for risks and recommended actions.
4. Export Data: Click "Download Professional Report" to save all analysis results, risks, and recommendations as a PDF. Note: Data is not saved on the platform after you close the session.

sequenceDiagram
    participant User
    participant UI
    participant ML_Model as ML Model
    participant AI_Agent as AI Agent
    participant PDF_Gen as PDF Generator

    User->>UI: Input Farm Data & Analyze
    UI->>ML_Model: Preprocess & Predict
    ML_Model-->>UI: Yield Prediction + Metrics
    UI->>AI_Agent: Analyze Risks & Generate Advice
    AI_Agent-->>UI: Structured Advisory Sections
    User->>UI: Click Download PDF
    UI->>PDF_Gen: Construct Document
    PDF_Gen-->>User: FarmIQ_Report.pdf

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🛠️ Installation & Setup

1. Environment Setup

# Clone the repository
git clone https://github.com/Amaan-pathan/Crop-Yield-Advisory.git
cd Crop-Yield-Advisory

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate

2. Install Dependencies

pip install -r requirements.txt

3. Run the Platform

streamlit run app.py

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💾 Technical Stack

• Frontend: Streamlit (with custom CSS & JS injection)
• Machine Learning: Scikit-Learn (Random Forest, Linear Regression)
• AI Agent: HuggingFace Transformers (Flan-T5) / Agentic logic
• Visualizations: Plotly Express
• Document Engine: FPDF2
• Data Handling: Pandas, Numpy

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⚖️ Disclaimer

FarmIQ is an AI-driven advisory tool. Predictions and recommendations are based on historical data patterns and generalized agronomy principles. Users should consult with a certified regional agronomist or soil specialist before making significant financial or operational farming decisions.

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👥 Contributors

• Pathan Amaan (Lead Developer & UI/UX Designer)
• Saad Arqam
• Priyabrata Singh
• Manu Pal