LLM Vision Fruit Disease Detection

Multi-LLM Vision Platform for Agricultural Disease Detection

A professional full-stack web application that leverages multiple Large Language Models (LLMs) with vision capabilities to detect diseases in fruits and vegetables through image analysis.

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Demo

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Screenshots - Results

Configuration Panel

Image Upload

Analysis in Progress

Results

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📊 Dataset

This project uses the Fruit and Vegetable Disease Dataset from Kaggle:

🔗 Download Dataset

Covered Fruits & Vegetables:

Fruits	Vegetables
🍎 Apple	🫑 Bell Pepper
🍌 Banana	🥕 Carrot
🍇 Grape	🥒 Cucumber
🍊 Orange	🥔 Potato
🍑 Peach	🍅 Tomato
🍐 Pear	🌽 Corn
🍍 Pineapple	🥬 Lettuce
🍓 Strawberry	🧅 Onion
🍉 Watermelon	🥦 Broccoli
🥭 Mango	🍆 Eggplant

Dataset Categories:

• ✅ Healthy — Fresh, disease-free samples
• ❌ Rotten/Diseased — Various disease conditions

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✨ Features

• 🤖 Multi-LLM Support — Choose from 5 AI vision models:

  • 🟣 Claude (Anthropic)
  • 🟢 GPT-4 Vision (OpenAI)
  • 🔵 Gemini Pro (Google)
  • 🟠 DeepSeek
  • ⚫ Grok (xAI)

• 🔐 Secure API Key Input — Password-protected field, never stored

• 📷 Drag & Drop Upload — Easy image upload interface

• 🔬 Comprehensive Analysis — Fruit identification, disease detection, severity assessment

• 💊 Actionable Recommendations — Treatment suggestions

• 🎨 Modern Dark UI — Professional, responsive design

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🛠️ Tech Stack

Frontend

Technology	Purpose
React 18	UI Framework
Vite	Build Tool
CSS3	Styling
JavaScript ES6+	Logic

Backend

Technology	Purpose
Python 3.9+	Runtime
Anaconda	Environment Management
FastAPI	API Framework
httpx	Async HTTP Client
Pydantic	Data Validation

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📁 Project Structure

llm-vision-fruit-analyzer/
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── Header.jsx
│   │   │   ├── ModelSelector.jsx
│   │   │   ├── ImageUploader.jsx
│   │   │   ├── ResultsPanel.jsx
│   │   │   └── Footer.jsx
│   │   ├── styles/
│   │   │   └── App.css
│   │   ├── App.jsx
│   │   └── main.jsx
│   ├── index.html
│   ├── package.json
│   └── vite.config.js
├── backend/
│   ├── main.py
│   └── requirements.txt
├── assets/
│   └── dashboard-preview.gif
├── Screenshots/
│   ├── screen_1.png
│   ├── screen_2.png
│   ├── screen_3.png
│   └── screen_4.png
├── .gitignore
├── LICENSE
└── README.md

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🚀 Quick Start

Prerequisites

• Anaconda or Miniconda
• Node.js 18+
• API key from at least one LLM provider

1. Clone Repository

git clone https://github.com/DaneshShokri94/llm-vision-fruit-analyzer.git
cd llm-vision-fruit-analyzer

2. Setup Backend (with Anaconda)

# Create conda environment
conda create -n fruit-analyzer python=3.10 -y
conda activate fruit-analyzer

# Install dependencies
cd backend
pip install -r requirements.txt

# Run server
python main.py

3. Setup Frontend (new terminal)

cd frontend
npm install
npm run dev

4. Open Application

Visit http://localhost:3000

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🔑 Getting API Keys

Provider	Get API Key
Anthropic (Claude)	console.anthropic.com
OpenAI (GPT-4)	platform.openai.com
Google (Gemini)	aistudio.google.com
DeepSeek	platform.deepseek.com
xAI (Grok)	x.ai

🦠 Detectable Diseases

Disease	Affected Fruits
Apple Scab	Apple
Black Rot	Apple, Grape
Citrus Canker	Orange, Lemon
Powdery Mildew	Grape, Strawberry
Anthracnose	Banana, Mango
Bacterial Spot	Tomato, Pepper
Late Blight	Potato, Tomato
Leaf Spot	Various

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🤖 Supported AI Models

Model	Provider	Best For
Claude	Anthropic	Detailed analysis, reasoning
GPT-4 Vision	OpenAI	General purpose, accurate
Gemini Pro	Google	Fast processing
DeepSeek	DeepSeek	Cost-effective
Grok	xAI	Real-time insights

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🔒 Security

• API keys are never stored on the server
• Keys are only used for the current session
• HTTPS recommended for production
• CORS configured for security

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📄 API Documentation

Once the backend is running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Endpoints

Method	Endpoint	Description
GET	/	Health check
GET	/models	List available models
POST	/analyze	Analyze fruit image

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🚀 Deployment

Frontend (Vercel/Netlify)

cd frontend
npm run build
# Upload dist folder

Backend (Railway/Render)

# Set environment variables
# Deploy backend folder

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🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing)
5. Open Pull Request

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📜 License

MIT License - see LICENSE file

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👤 Author

Danesh Shokri

• 🔗 LinkedIn: @danesh-shokri
• 🐙 GitHub: @DaneshShokri94
• 📧 Email: danesh.shokri.1@ulaval.ca

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📚 References

• Dataset: Fruit and Vegetable Disease Dataset by Muhammad Subhan
• Anthropic Claude API
• OpenAI GPT-4 Vision
• Google Gemini API

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⭐ Support

If you find this project useful, please give it a star!

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Built with ❤️ using React, FastAPI, Anaconda, and Multiple LLMs