AgriAgent AI 🌾🚜

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🌟 Overview

AgriAgent AI is an autonomous multi-agent advisory system for farmers.
It provides personalized insights into government schemes, crop insurance, loans, and sustainability incentives based on the farmer's profile.

With structured farm inputs and a knowledge base of 100+ crops and schemes, it delivers actionable recommendations through reports and an interactive RAG-based chatbot.

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🤖 Agents & Their Roles

• Profiling Agent 📝 – Collects and processes farm and farmer details.
• Subsidy Agent 💰 – Identifies eligible government schemes and subsidies.
• Insurance Agent 🛡️ – Evaluates and recommends crop insurance options.
• Sustainability Agent 🌱 – Recommends sustainable practices & carbon credit opportunities.
• Master Agent 🏆 – Aggregates all outputs and generates comprehensive advisory reports.

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⚡ Key Features

• Farmer Profile Collection – Captures farm size, location, crop type, soil type, irrigation method, and income category.
• Personalized Advisory Reports – Generates PDF reports 📄 with tailored recommendations on subsidies, insurance, loans, and sustainability practices.
• RAG-Powered Chatbot 🤖 – Provides instant answers to farmer queries using Retrieval-Augmented Generation.
• LLM Integration – Leverages OpenRouter API 🔑 for intelligent reasoning and response generation.
• Real-time Policy Updates – Uses SERP API 🌐 to fetch current government schemes and market information.
• Comprehensive Knowledge Base 📚 – Contains data on 100+ crops, subsidy schemes, insurance policies, and agricultural best practices.
• User-Friendly Interface – Built with HTML, CSS, and Bootstrap 🖥️ for an intuitive web experience.
• Cloud-Ready Architecture ☁️ – Environment variable-based configuration for secure API key management.

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🛠️ Technology Stack

Component	Technology
Backend	Flask (Python) 🐍
Frontend	HTML, CSS, Bootstrap 5 🖌️
Multi-Agent System	Python-based autonomous agents 🤖
LLM & RAG	OpenRouter API (for LLM) + SERP API (for real-time data) 🌐
Knowledge Base	Structured text files with crop & subsidy data 📚
Report Generation	Custom PDF generation service 📝
Deployment	Cloud-ready with environment variable configuration ☁️

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Live demo link 🌐 https://agri-agent-ai--AnkitaGhavate.replit.app

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🚀 Installation & Setup

Prerequisites

• Python 3.10 or higher
• OpenRouter API key
• SERP API key

Step-by-Step Installation

1. Clone the repository

   git clone https://github.com/yourusername/AgriAgentAI.git
   cd AgriAgentAI

2. Create a virtual environment

   python -m venv venv

3. Activate the virtual environment

   Windows:

   venv\Scripts\activate

   macOS/Linux:

   source venv/bin/activate

4. Install dependencies

   pip install -r requirements.txt

5. Set up environment variables

   Windows (Command Prompt):

   set OPENROUTER_API_KEY=your_openrouter_api_key
   set SERP_API_KEY=your_serp_api_key

   Windows (PowerShell):

   $env:OPENROUTER_API_KEY="your_openrouter_api_key"
   $env:SERP_API_KEY="your_serp_api_key"

   macOS/Linux:

   export OPENROUTER_API_KEY="your_openrouter_api_key"
   export SERP_API_KEY="your_serp_api_key"

6. Run the application

   python app.py

7. Access the web interface

   Open your browser and visit: http://localhost:5000 🌐

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📋 How It Works

1. Farmer Registration – Farmer enters details (land size, location, crops, soil type, etc.)
2. Profile Analysis – Profiling Agent processes the information
3. Multi-Agent Processing – Specialized agents analyze different aspects:
   • Subsidy Agent checks eligible government schemes
   • Insurance Agent evaluates crop insurance options
   • Financial Agent identifies loan opportunities
   • Sustainability Agent recommends eco-friendly practices
   • Climate Agent assesses weather-related risks
4. Report Generation – Master Agent compiles all insights into a comprehensive PDF report
5. Interactive Q&A – Farmers can ask follow-up questions to the RAG-powered chatbot

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🏗️ System Architecture

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🌱 Future Enhancements

• 🌦️ Real-time weather forecasting and climate risk alerts
• 📈 Market price prediction using Machine Learning
• 🦠 Crop disease detection using Computer Vision and Deep Learning
• 🌐 Multi-language support for regional farmers
• 📱 Android and iOS mobile application deployment
• 🏛️ Government portal integration for live scheme and subsidy updates
• 🤖 Advanced AI agents for personalized agricultural recommendations
• 🛰️ Satellite and IoT-based farm monitoring
• 💬 Voice-enabled AI assistant for farmers
• 📊 Predictive analytics for crop yield estimation

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👩‍💻 Developed By

Ankita Ghavate