Sustainable Agriculture Chatbot

Challenge Background

With increasing concerns over climate change, soil degradation, and water scarcity, sustainable agriculture is becoming essential for landowners. However, accessing personalized, science-backed recommendations remains a challenge. By leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), we can create a chatbot that delivers real-time, customized advice on sustainable farming. This chatbot will:

• Guide landowners on crop selection, soil health, irrigation, and biodiversity management
• Enhance responses with a custom knowledge database stored in a vector database Adapt recommendations based on user profiles and past interactions

The Problem

While agricultural knowledge is available, it is often scattered, generic, or difficult to interpret. Landowners need quick, actionable, and personalized guidance to make sustainable decisions.

Goal of the Project

1. Build a Knowledge Base – Collect high-quality agricultural data and store it in a vector database
2. Develop an AI Chatbot – Integrate an LLM with RAG for fact-based responses
3. Implement User Management – Track user interactions and refine recommendations
4. Host an API – Enable external applications to access chatbot insights Develop a Frontend – Provide an intuitive web-based interface

Project Timeline

What you'll learn

💡 Data Collection & Processing – Scraping, cleaning, and storing agricultural data 🤖 LLM & RAG – Using AI to enhance chatbot responses with factual data 📡 API Development – Creating an endpoint for chatbot queries 👥 User Management – Tracking user interactions for personalized insights 🌍 Web Development – Building a functional and user-friendly interface 🚀 Cloud Deployment – Launching the chatbot on a scalable infrastructure