This is a web application developed to help farmers in predicting the crop yield and to help them to improve their harvest. This is a machine learning based project which is built based on factors like rainfall, temperature, soil pH, nitrogen, potassium, phosphorous, name of the crop, state, season, area. On these various parameters the crop yield is calculated. In this project there are three units: 1. Frontend using REACT and CHAKRA-UI, 2. Backend using FLASK, 3. A machine learning model developed using Random Forest with Randomized Search CV.
I made a neat and systematic division of all the tasks to build this project. In this repository I made three folders and their respective README.md files in them . So refer those individual .md files to get a detailed understanding of them. After going through all the steps you can develop a intuitive web application. Also, after building the application you can host the application on the Internet without a single rupee.
I deployed frontend on netlify and the backend on render.com. As, I tried using AWS EC2, I was able to connect the backend from a local host only and the port obtained was a http but not a https. So, due to this reason I deployed this project on netlify and render.com. I mentioned the steps for deployement in the respective README.md files.
I conclude that this project gives you a great understanding of a react app built using chakra-ui, connecting a react app to a flask server and a minimalistic idea of handling ml model during deployement.