AgriML

AgriML is a web-based application of machine learning in the field of agriculture to provide personalized crop and fertilizer recommendations tailored to the unique soil composition of the farm.
The project leverages the power of machine learning and data science to enhance crop yield by analyzing the data and research done by established authorities.

Key Features

• Web-based application for ease of use.
• Minimal and responsive UI/UX.
• Multiple crop recommendations based on soil parameters.
• Precise fertilizer recommendation based on ICAR-IISS report on STCR.

Technologies Used

• Frontend: React JS + Vite, Tailwind CSS, React-Router
• API: Flask API
• Backend: Python, NumPy, Pandas, Scikit-learn

Getting started:

• Fork and Clone the repo using:

$ git clone https://github.com/paraschandra/AgriML.git

• Change the directory to frontend and install dependencies using:

$ npm i

• Now, change the directory to the backend and install all Python packages using:

$ pip install {package-name}

• Start the server by running app.py
• Run the web application at http://localhost:5173 using:

$ npm run dev

Screenshots

Contributors

Paras Chandra Purushottam Varshney