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track1_hexanova

🌿 AI Crop Disease Detection & Smart Advisory System

📌 Overview

This project is a web-based application that helps farmers and users detect crop diseases and receive actionable recommendations based on environmental conditions.

The system allows users to upload an image of a crop leaf, analyzes it using a backend service, and provides:

  • Disease identification
  • Weather-based insights
  • Risk level assessment
  • Treatment recommendations
  • Step-by-step action plan

🎯 Objective

The main goal of this project is to:

  • Assist farmers in early detection of crop diseases
  • Provide simple, accessible, and real-time agricultural insights
  • Reduce dependency on manual inspection and expert availability
  • Improve crop productivity through timely decisions

🚀 Features

  • 📷 Upload crop image
  • 🔍 Disease detection (currently simulated, extendable to ML models)
  • 🌡 Temperature & 💧 Humidity analysis
  • ⚠ Risk level prediction
  • 💊 Recommendation for treatment
  • 📅 Day-wise action plan
  • 🖥 Clean and user-friendly interface

🧱 Project Structure

project/ │ ├── backend/ │ ├── app.py │ ├── services.py │ └── frontend/ ├── index.html ├── upload.html ├── dashboard.html ├── main.js


⚙️ Technologies Used

Frontend

  • HTML
  • CSS (Bootstrap)
  • JavaScript

Backend

  • Python
  • Flask
  • Flask-CORS

Others

  • LocalStorage (for data transfer between pages)

🔄 Workflow

  1. User opens the application (index.html)
  2. Navigates to upload page
  3. Uploads crop image
  4. Image is sent to backend using POST request
  5. Backend processes:
    • Detects disease (dummy logic)
    • Fetches weather data
    • Generates recommendation
  6. Response is stored in browser (localStorage)
  7. User is redirected to dashboard
  8. Dashboard displays results

🧠 System Architecture

User → Upload Page → JavaScript (Fetch API) → Flask Backend → Services Layer → Decision Logic → JSON Response → Dashboard UI


🔧 Installation & Setup

Step 1: Clone the repository

git clone <your-repo-link>
cd project
Step 2: Install dependencies
pip install flask flask-cors requests


Step 3: Run backend

cd backend
python app.py

Step 4: Open frontend
Open index.html in browser
OR use Live Server

API Endpoint
POST /analyze
Request:
FormData with key: image
Response:
{
  "disease": "Tomato Leaf Curl",
  "confidence": 87,
  "temperature": 34,
  "humidity": 60,
  "risk": "High",
  "recommendation": "Use Neem oil spray",
  "timeline": [
    "Day 1-3: Neem oil spray",
    "Day 4: Monitor plant",
    "Day 5: Apply pesticide"
  ]
}

⚠️ Limitations
Disease detection is currently simulated (not real ML)
Requires internet for backend communication
Limited to predefined conditions

Future Enhancements
Integration of real Machine Learning models (CNN/TensorFlow)
Support for multiple crop types and diseases
Offline functionality for rural areas
Mobile application version
Multilingual support
Real-time weather API integration
Image history tracking

Target Users
Farmers
Agricultural students
Researchers
Agri-tech developers

Acknowledgements
OpenWeather API (optional future integration)
Flask Documentation
Bootstrap Framework

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