🌾 AgriVision AI

AI-Powered Agricultural Intelligence Platform

Empowering farmers with AI-driven insights for sustainable and profitable agriculture

Features • Installation • Tech Stack • Contributing

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📖 About

AgriVision AI is a comprehensive agricultural decision-support platform that leverages cutting-edge artificial intelligence to provide farmers with actionable insights. Built specifically for Indian farmers, the platform offers multilingual support and addresses real-world agricultural challenges through intelligent data analysis.

🎯 Problem Statement

Farmers face numerous challenges:

• Unpredictable crop yields
• Pest and disease identification
• Soil health management
• Market price volatility
• Language barriers in accessing technology

💡 Our Solution

AgriVision AI provides:

• AI-powered crop yield predictions using multi-factor analysis
• Intelligent pest & disease diagnosis from plant images
• Soil analysis & recommendations for optimal crop selection
• Real-time weather data integration
• Market price tracking for informed selling decisions
• Multilingual interface (English, Hindi, Odia, Telugu)
• Interactive field management with map-based tools

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✨ Features

🌱 Core Features

Feature	Description
🎯 Yield Prediction	AI-powered predictions using field data, weather, and soil conditions
🐛 Pest & Disease Detection	Image-based diagnosis with treatment recommendations
🧪 Soil Analysis	NPK analysis with crop recommendations and fertility reports
🗺️ Field Management	Interactive map-based field drawing and area calculation
🌤️ Weather Integration	Real-time weather data with 7-day forecasts
💰 Market Prices	Live market price tracking for major crops
🌍 Multilingual	Support for 4 Indian languages with text-to-speech
📊 Analytics Dashboard	Comprehensive insights and data visualization

🎨 Design Features

• Premium Glass Morphism - Modern, depth-rich UI
• Advanced Gradients - Multi-color transitions and mesh backgrounds
• Smooth Animations - 60fps GPU-accelerated effects
• Dark Mode - Fully optimized dark theme
• Responsive Design - Works on all devices
• Accessibility - WCAG compliant with screen reader support

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🚀 Installation

Prerequisites

• Node.js 20.x or later
• npm or yarn
• Google Gemini API Key (Free tier available)

Quick Start

1. Clone the repository

git clone https://github.com/Dhuvie/agrivision-ai.git
cd agrivision-ai

2. Install dependencies

npm install
# or
yarn install

3. Set up environment variables

Create a .env.local file in the root directory:

# Google Gemini API Key (Required)
GEMINI_API_KEY=your_gemini_api_key_here

Get your API key:

• Visit Google AI Studio
• Click "Create API key"
• Copy and paste into .env.local

4. Run the development server

npm run dev
# or
yarn dev

5. Open your browser

Navigate to http://localhost:9002

Build for Production

npm run build
npm start

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🛠️ Tech Stack

Frontend

• Next.js 15.3 - React framework with App Router
• TypeScript - Type-safe development
• Tailwind CSS - Utility-first styling
• Radix UI - Accessible component primitives
• Lucide React - Beautiful icons
• Recharts - Data visualization
• Leaflet - Interactive maps

AI & Backend

• Google Gemini - Generative AI models
• Custom ML Model - Crop prediction (Python/FastAPI)
• FastAPI - Python backend for ML predictions

State Management

• React Context API - Global state
• Local Storage - Data persistence
• React Hook Form - Form management
• Zod - Schema validation

Development Tools

• ESLint - Code linting
• Prettier - Code formatting
• TypeScript - Type checking

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📁 Project Structure

agrivision-ai/
├── src/
│   ├── app/                    # Next.js App Router
│   │   ├── (routes)/          # Application routes
│   │   ├── api/               # API routes
│   │   ├── globals.css        # Global styles
│   │   └── layout.tsx         # Root layout
│   ├── components/
│   │   ├── agrivision/        # Feature components
│   │   └── ui/                # Reusable UI components
│   ├── contexts/              # React contexts
│   │   ├── FieldsContext.tsx  # Field management
│   │   └── LanguageContext.tsx # Internationalization
│   ├── lib/
│   │   ├── translations.ts    # i18n translations
│   │   └── utils.ts           # Utility functions
│   └── ai/
│       ├── flows/             # AI workflows
│       └── prompts/           # AI prompts
├── public/                    # Static assets
├── .env.local                 # Environment variables
├── package.json               # Dependencies
├── tailwind.config.ts         # Tailwind configuration
└── tsconfig.json             # TypeScript configuration

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🌍 Multilingual Support

AgriVision AI supports 4 languages:

Language	Code	Status
🇬🇧 English	en	✅ Complete
🇮🇳 Hindi (हिंदी)	hi	✅ Complete
🇮🇳 Odia (ଓଡ଼ିଆ)	or	✅ Complete
🇮🇳 Telugu (తెలుగు)	te	✅ Complete

Adding New Languages

1. Add translations to src/lib/translations.ts
2. Update the Translation type
3. Add language option in Settings

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🎨 Design System

Color Palette

/* Primary (Green) */
--primary: 142 70% 45%;

/* Secondary (Blue) */
--secondary: 221 83% 53%;

/* Accent (Amber) */
--accent: 43 74% 66%;

Components

All components follow:

• Accessibility standards (WCAG 2.1)
• Responsive design (mobile-first)
• Dark mode support
• Animation best practices

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🤝 Contributing

We welcome contributions! Here's how you can help:

Ways to Contribute

• 🐛 Report bugs - Open an issue
• 💡 Suggest features - Share your ideas
• 📝 Improve documentation - Fix typos, add examples
• 🌍 Add translations - Support more languages
• 💻 Submit code - Fix bugs, add features

Development Workflow

1. Fork the repository
2. Create a feature branch

   git checkout -b feature/amazing-feature

3. Make your changes
4. Commit with clear messages

   git commit -m "Add: Amazing new feature"

5. Push to your fork

   git push origin feature/amazing-feature

6. Open a Pull Request

Code Style

• Follow existing code patterns
• Use TypeScript for type safety
• Write meaningful commit messages
• Add comments for complex logic
• Test your changes thoroughly

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📝 API Documentation

AI Flows

Yield Prediction

import { predictYieldFromPrompt } from '@/ai/flows/predict-yield-flow';

const result = await predictYieldFromPrompt({
  fieldDescription: "5 acre wheat field",
  weatherData: { temp: 25, humidity: 70 },
  soilData: { N: 50, P: 40, K: 30 }
});

Pest Detection

import { diagnosePestDisease } from '@/ai/flows/diagnose-pest-disease-flow';

const diagnosis = await diagnosePestDisease({
  imageUrl: "data:image/jpeg;base64,...",
  cropType: "wheat"
});

Custom ML Model

The Python FastAPI backend provides:

# Endpoint: POST /predict
{
  "N": 50,
  "P": 40,
  "K": 30,
  "temperature": 25,
  "humidity": 70,
  "ph": 6.5,
  "rainfall": 100
}

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🔒 Security

• API Keys - Never commit .env.local to version control
• Input Validation - All user inputs are validated with Zod
• XSS Protection - React's built-in protection
• CORS - Configured for production domains

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📊 Performance

Lighthouse Scores

Metric	Score
Performance	95+
Accessibility	100
Best Practices	100
SEO	100

Optimizations

• ⚡ Next.js App Router - Automatic code splitting
• 🖼️ Image Optimization - Next/Image component
• 🎨 CSS-in-JS - Tailwind CSS (JIT)
• 📦 Bundle Size - Tree shaking enabled
• 🚀 Edge Functions - Deployed on Vercel Edge

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🧪 Testing

# Run type checking
npm run typecheck

# Run linting
npm run lint

# Build test
npm run build

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👥 Team

Built with ❤️ for Indian Farmers

AgriVision AI Team

GitHub

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🙏 Acknowledgments

• Google Gemini - For powerful AI capabilities
• Next.js Team - For the amazing framework
• Radix UI - For accessible components
• Indian Farmers - For inspiring this project

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⭐ Star us on GitHub!

If you find AgriVision AI helpful, please consider giving us a star ⭐

Made with 🌾 for a better agricultural future