Agri Connect 🌿🔗

🔥 Inspiration

Agri Connect was inspired by the desire to create a comprehensive platform that addresses the diverse agricultural needs of farmers. We recognized the challenges faced by farmers in accessing relevant information, market trends, and agricultural assistance, and sought to develop a solution that would streamline these processes and empower farmers to make informed decisions.

❓ What it does

Agri Connect is a multifaceted platform that offers a range of features to support farmers in various aspects of agriculture:

• Customized Assistance: Our platform provides personalized crop recommendations based on soil fertility and weather reports, helping farmers optimize their yields.
• Market Trends and Prices: We leverage open APIs to deliver current market trends and prices, enabling farmers to make informed decisions about their produce.
• Multilingual Support: Agri Connect offers multilingual support in Telugu, Hindi, and English, catering to the diverse linguistic preferences of users. We have plans to expand to more languages in the future.
• AI Chatbot: Utilizing OpenAI, our chatbot is fine-tuned to provide relevant and helpful responses in multiple languages, enhancing user experience and accessibility.
• Social Platform: Similar to Facebook, Agri Connect allows users to share their farming experiences, updates, and challenges, fostering a supportive farming community.
• Marketplace: As a future enhancement, we plan to introduce a marketplace where farmers can showcase their products and retailers can offer machinery for rental and pesticides, promoting transparency and inclusivity in agricultural transactions.

🛠 How we built it

Agri Connect is built on the MERN stack (MongoDB, Express.js, React.js, Node.js), providing a robust and scalable architecture for our platform. Additionally, we integrated machine learning APIs, such as crop recommendation, which are developed in Flask.

🦾 Challenges we ran into

• Integrating Multiple APIs: One of the challenges we faced was integrating various APIs seamlessly to provide comprehensive features without compromising user experience.
• Multilingual Support: Implementing multilingual support posed challenges in terms of UI/UX design and language processing, but we overcame these hurdles through thorough testing and iteration.
• Scaling and Performance: Ensuring scalability and optimal performance, especially with the inclusion of AI chatbots and marketplace features, required careful planning and optimization.

🐱‍🏍 Accomplishments we are proud of

• Successfully implementing personalized crop recommendation based on soil fertility and weather data.
• Providing multilingual support to cater to a diverse user base.
• Developing an AI chatbot that delivers reasonable outputs in multiple languages.
• Creating a user-friendly social platform for farmers to connect and share their experiences.

📑 What we learned

• API Integration: We gained valuable experience in integrating various APIs into our platform, learning best practices for smooth functionality.
• Multilingual Support: Implementing multilingual support taught us the importance of considering linguistic diversity in user interaction design.
• Machine Learning in Agriculture: Building machine learning models for crop recommendation enhanced our understanding of applying AI in agriculture for improved productivity.

📢 What's next for Agri Connect

• Enhanced Marketplace: We aim to further develop the marketplace feature, facilitating transparent transactions between farmers and retailers.
• Expanded Language Support: Continuously expanding our language support to reach more farmers across different regions.
• Community Building: Strengthening the agricultural community by introducing forums, expert Q&A sessions, and knowledge-sharing initiatives within the platform.

Screenshots

Run Locally

Clone the project

  git clone https://github.com/ashishnallana/AgriConnect.git

Go to the project directory

  cd AgriConnect

Install dependencies (client)

  cd client
  npm install

Install dependencies (server - MERN)

  cd server
  npm install

Install dependencies (ml-server - FLASK)

  cd ml-server
  pip install

Environment Variables

To run this project, you will need to add the following environment variables to your .env files

client (Vite + React)

• VITE_API: URL for the main server.
• VITE_ML_API: URL for the Flask ML server.
• VITE_OPENWEATHERMAP_API_KEY: API key for OpenWeatherMap.
• VITE_FB_APIKEY: Firebase API key.
• VITE_FB_AUTHDOMAIN: Firebase authentication domain.
• VITE_FB_PROJECTID: Firebase project ID.
• VITE_FB_STORAGEBUCKET: Firebase storage bucket.
• VITE_FB_MESSAGINGSENDERID: Firebase messaging sender ID.
• VITE_FB_APPID: Firebase app ID.

server (MERN)

• PORT: Port number for the server.
• MONGO_URL: MongoDB connection string.
• JWT_SECRET: Secret key for JWT authentication.

ml-server (FLASK)

• OPENAI_API_KEY: API key for OpenAI.

Start the server (client + server)

  cd server
  npm run dev

Start the ml-server (FLASK)

  cd ml-server
  python main.py

Authors

• @ashishnallana
• @Yaswanth14
• @sahithigurlinka3