The problem Cropify solves

India is an agricultural country, though the agro-industry has a total of 12-13% of the GDP share. More than 60% of rural India depends on agricultural yield growth and allied agro-industry products. Crop yield prediction is an essential task for the decision-makers at national and regional levels for rapid decision-making. An accurate crop yield prediction model can help farmers to decide on what to grow and when to grow. It will act as a medium to provide the farmers with efficient information required to get high yields and thus maximize profits which in turn will reduce the suicide rates and lessen difficulties.

Hence our team came up with Project Cropify: Crop yield diagnostic tool !

Web application with integrated machine learning model developed in order to provide farmers an approximation on how much amount of crop will be produced depending upon the given input.
This will help the farmers to know the crop yield in advance to plan and choose a crop that would give a better yield.
The ML model aims to help farmers to cultivate proper crops for better yield production. To precisely predict the crop yield it analyzes factors like district (assuming same weather and soil parameters in a particular district), state season, and crop type. It can be achieved using supervised and unsupervised learning algorithms.

Challenges we ran into

• We faced difficulty in testing the data sets.
• Debugging issues and dynamic routing in React.js.
• Firebase authentication
• Data Cleaning and data gathering were very time-consuming.