Skip to content

Latest commit

 

History

History
92 lines (65 loc) · 4.42 KB

README.md

File metadata and controls

92 lines (65 loc) · 4.42 KB

🌱 A Digital बंधू for Our Farmers

Bridging educational, geographical, and language barriers to support India's smallholder farmers.

Hack This Fall - 2024

  • Team Name: Code Stars
  • Project Title: कृषिबंधू (Krishi-Bandhu)

🚜 Problem Statement

Agriculture forms the backbone of the Indian economy, with over half of the population dependent on it. Smallholder farmers, those with less than 5 acres of land, contribute to 2/3rds of the world’s food production and makeup 86% of all Indian farmers. Yet, these farmers often face challenges in:

Adopting sustainable practices due to limited access to real-time data. Managing resources effectively with minimal guidance on technological and scientific advancements. Getting accurate weather forecasts, managing crop health, and adapting to climate changes. No integrated solution currently exists to meet all these needs in one place, which is where कृषिबंधू steps in.

💡 Solution

कृषिबंधू (Krishi-Bandhu) is designed to empower smallholder farmers through cutting-edge technology. Here's how it helps:

  1. Crop Yield Prediction: Uses machine learning to predict crop yield, helping farmers plan better.
  2. Early Crop Health Diagnostics: AI-driven diagnostics to detect diseases early, minimizing crop loss.
  3. Climate Adaptation Support: Provides data to help farmers make informed decisions based on changing climate conditions.
  4. Multilingual Chatbot: With speech-to-text support, this chatbot guides farmers on agriculture-related queries, government schemes, and crop rates in their preferred language.
  5. Weather Alerts via SMS and Calls: Provides real-time weather updates offline through calls and SMS, helping farmers prepare for adverse conditions.

Through these features, कृषिबंधू enables data-driven decision-making, efficient resource management, and a sustainable future for agriculture.

⚙️ Technologies Used

  • Frontend: JavaScript, Bootstrap, React.js, TensorFlow.js
  • Backend: Python, Flask, Gemini API
  • Database: MongoDB, Chroma
  • Messaging: Twilio for Calls and SMS alerts

🚧 Challenges We Faced

  1. Offline Execution of ML Model for Plant Disease Detection

    • Challenge: Running ML models locally presented memory constraints on some devices.
    • Solution: We optimized the model using TensorFlow.js and employed progressive loading to improve performance.
  2. Integrating Twilio for SMS Alerts

    • Challenge: Faced rate limits and reliability issues with SMS delivery.
    • Solution: Configured Twilio settings and optimized our API calls to manage rate limits effectively.

🛤️ GitHub Education: Best Use of GitHub Track

Our project leverages GitHub's tools to the fullest, incorporating:

  • GitHub Copilot for faster and more efficient coding.
  • Feature branches, pull requests, and merge commits to streamline team collaboration.

GitHub was essential to our project’s success, enabling seamless teamwork and rapid development.

📸 Demo

📷 Project Screenshots

Automated Alerts Plant Disease Detection Crop Yeild Predictor
Local Market Price Predictor Chatbot

🎉 Join Us in Building a Sustainable Future!

कृषिबंधू is more than just an app; it's a step towards empowering farmers with the knowledge and tools they need to succeed. Whether you're a developer, researcher, or simply a supporter of sustainable agriculture, we’d love to have you onboard!