CropMate - MERN, React Native, Flask, Machine Learning

CropMate is a comprehensive agricultural project aimed at revolutionizing farming practices by leveraging technology and data-driven insights. It consists of several components, including a React web application, a mobile application developed using React Native, a server-side MERN Stack backend, and ensembled machine learning model for precise recommendation for the crops based on soil parameters.

Features

• Crop Recommendation: Utilizes machine learning algorithms to provide personalized crop recommendations based on soil and environmental data.
• RestAPIs: Single API calls for both web app and mobile app
• Secure: The application is secured with Brypt and JWT libraries.
• Community Forum: Facilitates knowledge sharing and collaboration among farmers through a dedicated forum.
• User Authentication: Users can sign up, log in, and log out securely.

Technologies Used

• MongoDB: NoSQL database used for storing user data, posts, and other information.
• Express.js: Web application framework for building APIs and handling HTTP requests.
• React.js: Frontend library for building user interfaces.
• Node.js: JavaScript runtime environment used for server-side logic.
• Mongoose: MongoDB object modeling tool for Node.js.
• React Native: UI software framework used to develop applications for Android, iOS.
• JWT (JSON Web Tokens): Used for user authentication and authorization.
• Bcrypt: Used for encrypting user passwords.
• Mongoose: MongoDB object modeling tool for Node.js.
• Flask: Micro web framework written in Python used to load and run the ML model and interact with it.

Project Structure

The project is organized into the following folders:

• App: Contains the code for the mobile application developed using Expo and React Native.
• Server: Houses the backend server implementation using the MERN stack (MongoDB, Express.js, React.js, Node.js).
• Client: Contains the code for the web application frontend, developed using React.js.
• ML: Includes the machine learning module responsible for crop recommendation, implemented using Python.

How to run

To run CropMate on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/your-username/cropmate.git
   cd cropmate

2. Install Dependencies

• Server

  cd server
  npm install

• Client

  cd client
  npm install

• App

  cd app
  npm install

• ML

  pip install numpy
  pip install pandas
  pip install sklearn
  pip install requests
  pip install pickle

4. Set Up MongoDB Ensure you have MongoDB installed and running on your system. Update the MongoDB connection string in the server code if necessary.

5. Start the Servers

• Server

  cd server
  npm start

• Client

  cd client
  npm run dev

• App

  cd app
  npx expo start

• ML

  cd ml
  python app.py 

6. Access the Application Web App: Open your web browser and go to http://localhost:3000. Mobile App: Use the Expo app to scan the QR code generated after running the Expo server.