🌱 AI-Powered Potato Crop Growth Stage & Nutrient Health Management

📌 Problem Statement

Develop an AI solution that uses satellite imagery to simultaneously detect growth stages of potato crops and map nutrient health (e.g., Nitrogen levels).

The system should deliver precise, stage-specific irrigation and fertilizer recommendations to:

• Maximize yield
• Optimize resource use
• Reduce costs
• Improve long-term soil health

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🚜 Why It Matters

• Incorrect timing of irrigation or fertilization can drastically reduce yields.
• Inefficient nutrient management increases costs and degrades soil quality.
• An integrated growth stage–nutrient health approach ensures resources are applied at the right time, in the right quantity, and to the right zones.

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📂 Data Sources & APIs

• 🌍 Sentinel-2 satellite imagery
• 🌱 NDVI/NDRE vegetation indices
• 🧪 Soil fertility datasets
• 🌦 Historical yield and weather data (optional for refinement)

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🎯 Prototype Goals

• Process sample satellite images of potato fields
• Classify fields into at least 3 growth stages
• Highlight low-fertility zones using NDVI/NDRE analysis

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🏗️ System Architecture

flowchart TD
  %% Farmer Input
  subgraph Farmer_Input [Farmer Input]
    A(Start) --> B[Farmer logs in & provides field data]
    B --> C[1. Field Location & Boundaries]
    B --> D[2. Uploads Soil Health Card]
  end

  %% Automated Backend Pipeline
  subgraph Backend_Pipeline [Automated Backend Pipeline]
    E[Weekly Scheduler]
    E --> F[Fetch Sentinel-2 Satellite Images]
    E --> G[Fetch Weather & Historical Data]
  end

  %% AI Analysis & Processing
  subgraph AI_Analysis [AI Analysis & Processing]
    H((AI Core))
    C --> H
    D --> H
    F --> H
    G --> H
    H --> I[Process Images:<br>Calculate NDVI/NDRE]
    I --> J([ML Model:<br>Classify Growth Stage])
    I --> K([ML Model:<br>Map Nutrient Health - N, P, K])
  end

  %% Recommendation Engine
  subgraph Recommendation_Engine [Recommendation Engine]
    L[MCP:<br>Master Control Program]
    J --> L
    K --> L
    L --> M[Agentic AI:<br>Generate Actionable Insights]
    M --> N{Create Zone-wise<br>Irrigation Plan}
    M --> O{Create Stage-Specific<br>Fertilizer Plan}
    M --> P{Generate Actionable Alerts}
  end

  %% Farmer-Facing App
  subgraph Farmer_App [Farmer-Facing App]
    Q[Integrated Dashboard]
    N --> Q
    O --> Q
    P --> Q
    Q --> R[Display Interactive Health Map]
    Q --> S[Show Recommendations & Crop Stage]
    Q --> T[Push Notifications & Alerts]
  end

  T --> U(End)

  %% Styling for start & end
  style A fill:#4CAF50,color:#fff,stroke:#388E3C,stroke-width:2px
  style U fill:#F44336,color:#fff,stroke:#D32F2F,stroke-width:2px

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🧠 Core Components

🧠 AI & ML Models Used

• MCLSTM (Multivariate Convolutional LSTM): For capturing temporal + spatial crop growth changes
• DeepCGM (Deep Crop Growth Model): For growth stage classification & nutrient health estimation
• Classification Model (Random Forest / XGBoost): Fertilizer usage recommendation

🔹 Data-Driven Insights

• NDVI/NDRE analysis for vegetation health.
• Soil fertility overlays for nutrient deficiencies.
• Historical yield-weather fusion for better predictions.

🔹 Optimized Potato Yield

• Stage-specific irrigation recommendation.
• Nitrogen-level mapping for targeted fertilizer use.
• Precision farming for higher yield with lower cost.

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📊 Correlation Matrix

Understanding the relationships between variables in our dataset.

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🧹 Pre-processed Data

Sample of the cleaned and structured dataset used for training.

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✅ Model Output

Accuracy results of the fertilizer recommendation model.