🌾 CropLab - AI-Powered Crop Health Prediction and Monitoring System

🚀 Overview

CropLab is an innovative agricultural technology solution that leverages satellite imagery, artificial intelligence, and machine learning to provide farmers with real-time crop health monitoring, yield predictions, and actionable farming insights. Our platform combines cutting-edge remote sensing technology with user-friendly interfaces to make precision agriculture accessible to farmers of all scales.

✨ Features

🛰️ Satellite-Based Analysis: Real-time crop health monitoring using satellite imagery
🤖 AI-Powered Predictions: Machine learning models for yield prediction and risk assessment
📊 Interactive Dashboards: Comprehensive visualization of farm data and analytics
🔔 Smart Notifications: Automated alerts for crop health issues and farming recommendations
📱 Multi-Device Access: Seamless experience across desktop, tablet, and mobile devices
🗺️ Interactive Mapping: Detailed farm boundary mapping with overlay visualizations
📈 Historical Analysis: Track farm performance and trends over time
🌱 Guest Mode: Try the platform without registration for immediate access

🚀 Getting Started

Guest Mode Experience

New users can immediately start exploring the platform without any registration required. The guest mode provides full access to farm creation and analysis features, making it easy to evaluate the platform's capabilities.

👤 User Journey

1. Farm Creation

Users begin their journey by creating a farm profile. The intuitive interface guides them through the process of defining farm boundaries and basic information.

Step-by-Step Farm Setup

The farm creation process involves:

Basic Information: Farm name, crop type, planting and harvest dates
Boundary Definition: Interactive map tool for precise boundary drawing
Area Calculation: Automatic calculation of farm area in hectares
Location Services: GPS-based location detection and address resolution

2. AI Analysis in Progress

Once a farm is created, our AI engines immediately begin processing satellite data to generate comprehensive crop health analysis.

The analysis includes:

NDVI Processing: Normalized Difference Vegetation Index calculation
Health Segmentation: Classification of crop areas into healthy, moderate, and stressed zones
Yield Prediction: ML-based yield forecasting using historical and current data
Risk Assessment: Identification of potential issues and stress factors

3. Comprehensive Results

📊 API Response Structure

Our generate_heatmap API returns comprehensive analysis data in the following JSON format:

{
  "predicted_yield": 4.314789772033691,
  "old_yield": 4.75,
  "growth": {
    "ratio": 0.9083767941123561,
    "percentage": -9.162320588764391
  },
  "location": {
    "district": "moga",
    "coordinates": {
      "latitude": 30.686323800000004,
      "longitude": 74.95473419999999
    },
    "complete_address": "Bagha Purana Tahsil, Moga, Punjab, India"
  },
  "ndvi_shape": [315, 316],
  "sensor_shape": [315, 316, 5],
  "masks": {
    "red_mask_base64": "mask in base64",
    "yellow_mask_base64": "mask in base64",
    "green_mask_base64": "mask in base64"
  },
  "pixel_counts": {
    "valid": 99540,
    "red": 2785,
    "yellow": 15194,
    "green": 81561
  },
  "thresholds": {
    "t1": 0.5,
    "t2": 0.75
  },
  "suggestions": {
    "overall_assessment": "⚠️ Average. Some areas need improvement.",
    "yield_analysis": {
      "predicted_yield": 4.31,
      "previous_yield": 4.75,
      "yield_change": -0.44,
      "yield_change_percent": -9.2,
      "status": "Lower"
    },
    "field_management": [
      "🟢 Great! Most of your field looks healthy.",
      "Keep following your current farming practices."
    ],
    "soil_recommendations": [
      "🧪 Soil is alkaline — use gypsum or organic manure.",
      "🧂 High soil salinity — improve drainage and apply gypsum."
    ],
    "immediate_actions": [
      "✅ No urgent action required — continue regular monitoring."
    ],
    "seasonal_planning": [
      "🌾 Punjab: Plan better wheat-rice rotation.",
      "💧 Prepare for water management before monsoon.",
      "📅 Maintain yield records.",
      "🌱 Try intercropping to improve productivity."
    ],
    "risk_alerts": [
      "✅ No major problems detected — field is in good condition."
    ]
  }
}

For detailed technical implementation and ML model analysis, see our Deep Dive Technical Documentation.

🔄 Technology Flow

System Architecture Flowchart

Data Processing Pipeline

flowchart TD
    A[User Creates Farm] --> B[Farm Coordinates Stored]
    B --> C[Satellite Data Request]
    C --> D[Google Earth Engine API]
    D --> E[NDVI Data Processing]
    E --> F[TensorFlow ML Model]
    F --> G[Health Classification]
    G --> H[Yield Prediction]
    H --> I[Generate Masks & Visualizations]
    I --> J[Suggestions Generation]
    J --> K[Results Display]
    K --> L[Store Analysis Results]
    
    M[Background Scheduler] --> N[2-Day Reprocessing]
    N --> O[Compare Previous Results]
    O --> P{Changes Detected?}
    P -->|Yes| Q[Generate Alerts]
    P -->|No| R[Continue Monitoring]
    Q --> S[Send Notifications]
    
    style A fill:#e1f5fe
    style F fill:#fff3e0
    style G fill:#f3e5f5
    style K fill:#e8f5e8
    style Q fill:#ffebee

Technical Processing Flow

Data Acquisition: Satellite imagery retrieval from Google Earth Engine
Preprocessing: Image normalization and coordinate transformation
NDVI Calculation: Vegetation index computation for health assessment
ML Processing: TensorFlow model inference for pattern recognition
Classification: Pixel-level health categorization (Red/Yellow/Green zones)
Prediction: Yield forecasting based on current conditions
Visualization: Mask generation and overlay creation
Insights: AI-generated recommendations and action items

🔐 User Authentication & Continuous Monitoring

Enhanced Features for Registered Users

While guest mode provides full analytical capabilities, registered users unlock additional premium features:

🔔 Smart Notification System

Real-time Alerts: Immediate notifications for critical crop health changes
Scheduled Monitoring: Automated farm reprocessing every 2 days
Multi-channel Delivery: Email, SMS, and in-app notifications
Custom Thresholds: Personalized alert settings based on crop and season

📱 Cross-Device Synchronization

Cloud Storage: Farm data synchronized across all devices
Offline Access: Download reports for offline viewing
Collaborative Features: Share farm insights with team members
Historical Tracking: Long-term trend analysis and performance metrics

🔄 Automated Monitoring Cycle

flowchart LR
    A[Day 0: Initial Analysis] --> B[Day 2: Scheduled Reprocess]
    B --> C{Significant Changes?}
    C -->|Yes| D[Generate Alert]
    C -->|No| E[Continue Normal Monitoring]
    D --> F[Send Notification]
    F --> G[User Reviews Changes]
    E --> H[Day 4: Next Cycle]
    H --> B
    
    style D fill:#ffcdd2
    style F fill:#fff3e0

🎯 Key Benefits

For Farmers

Early Problem Detection: Identify crop stress before visible symptoms appear
Optimized Resource Use: Targeted interventions reduce waste and costs
Yield Maximization: Data-driven decisions for better harvest outcomes
Risk Mitigation: Proactive management of potential crop issues

For Agricultural Consultants

Scalable Monitoring: Manage multiple farms from a single dashboard
Evidence-Based Advice: Satellite data supports recommendations
Client Reporting: Professional reports with visual evidence
Efficiency Gains: Reduce field visits through remote monitoring

For Agricultural Organizations

Regional Monitoring: Track crop health across large areas
Policy Support: Data-driven insights for agricultural planning
Research Applications: Historical data for agricultural research
Resource Allocation: Optimize support based on actual needs

🛠️ Technology Stack

Frontend

React: Modern UI framework for building interactive user interfaces
TypeScript: Type-safe development for better code quality
MongoDB: Document-based database for flexible data storage
Leaflet: Interactive mapping and geospatial visualization
Vite: Fast development and build tooling

Backend

Node.js: JavaScript runtime for server-side development
Express.js: Web framework for API development
Kafka: Distributed streaming platform for real-time data processing
JWT: Secure authentication and authorization

AI/ML Services

MATLAB: Advanced mathematical computing and algorithm development
TensorFlow: Machine learning model training and inference
Python: Core language for AI/ML processing
Numpy: Numerical computing library
Pandas: Data manipulation and analysis tools
Matplotlib: Visualization library for data representation
SciPy: Scientific computing module for advanced algorithms
Azure Functions: Serverless compute service for AI functions
AWS Lambda: Serverless execution environment for AI tasks

Data Sources

Google Earth Engine: Satellite imagery and geospatial data
OpenWeather: For Weather data integration

📦 Installation

Prerequisites

Node.js 18+ and npm
Python 3.8+ and pip
MongoDB (local or Atlas)
Google Earth Engine account

Quick Start

Clone the repository

git clone https://github.com/Pratham2703005/CropLab.git
cd CropLab

Install Frontend Dependencies
```
cd Frontend
npm install
npm run dev
```
Install Backend Dependencies
```
cd ../Backend
npm install
npm run dev
```
Setup AI Service
```
NOT HOSTED YET, so apologies!!
```

Environment Configuration

# Backend .env
MONGODB_URI=your_mongodb_connection_string
JWT_SECRET=your_jwt_secret

# AI Service .env
GOOGLE_APPLICATION_CREDENTIALS=path_to_service_account.json

📚 API Documentation

Core Endpoints

Farm Management

GET /api/farms - Retrieve user farms
POST /api/farms - Create new farm
PUT /api/farms/:id - Update farm details
DELETE /api/farms/:id - Delete farm

Analysis Services

POST /generate_heatmap - Generate crop health analysis
GET /api/farms/:id/history - Retrieve analysis history
POST /api/farms/:id/predict - Get yield predictions

User Management

POST /api/auth/register - User registration
POST /api/auth/login - User authentication
GET /api/auth/profile - User profile data

WebSocket Events

farm_analysis_complete - Real-time analysis updates
alert_generated - Immediate alert notifications
monitoring_update - Scheduled monitoring results

👥 Our Team

Meet the dedicated team behind CropLab - passionate developers, data scientists, and agricultural technology enthusiasts working together to revolutionize farming through AI and satellite technology.

🚀 Core Development Team

Name	Role	LinkedIn
Madhav Chaturvedi(Cap)	Full Stack Dev
Nauman Hussain	Backend Dev
Shikher Jain	AI/ML Engineer
Aleena Khan	Frontend Dev
Sheeba Salim	AI/ML Engineer
Aqsa Mushir	AI/ML Engineer
Me	Full Stack Dev

🌟 Team Contributions

🔬 Research & Development: Extensive research in precision agriculture and satellite data analysis
💻 Technical Implementation: Full-stack development with modern technologies and best practices
🤖 AI/ML Innovation: Advanced machine learning models for crop yield prediction and health analysis
🎨 User Experience: Intuitive design and seamless user interface for farmers and agricultural consultants
☁️ Infrastructure: Scalable cloud deployment and robust system architecture
📊 Data Engineering: Efficient processing of satellite imagery and agricultural datasets

Our diverse team combines expertise in computer science, agricultural engineering, data science, and user experience design to create impactful solutions for sustainable agriculture.

🙏 Acknowledgments

Google Earth Engine for satellite imagery access
TensorFlow team for machine learning frameworks
Agricultural research communities for domain expertise
Open source contributors and maintainers

Built with ❤️ for sustainable agriculture and food security

Name		Name	Last commit message	Last commit date
Latest commit History 51 Commits
.github/workflows		.github/workflows
.vscode		.vscode
Backend		Backend
Frontend		Frontend
assets		assets
.DS_Store		.DS_Store
GENERATE_HEATMAP.md		GENERATE_HEATMAP.md
README.md		README.md

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