Inspiration
To bring change,efficiency and ease to a farmer's life.
What it does
Our project describes a machine learning model (Described in Tree and Yield HTML Files) that predicts the best crop for given soil properties and using that along with the rainfall can predict the output yield in kg/hectare of the specified crop. Depending on the yield the government may determine which seed to send to each farmer also how much insurance to provide. Higher the yield the more the premium
How we built it
We made use of the jupyter notebook ecosystem from the Anaconda environment to train and test our model. We made use of a public dataset by ICRISAT and cleaned it to our needs. We made use of a web interface to interact with the farmer.
Challenges we ran into
1) Lack of authentic and accurate data 2) Deciding how to clean our model. 3) Parameter selection
Accomplishments that we're proud of
Our Models that can accurately predict the best crop to use. Our second model which can tell the estimated yield based on rainfall that is estimated in the future.
What we learned
1) The intricacies of how much effort the farmer puts into deciding which crop to grow which we often take for granted. 2) How to get together as a team and set our sights to a solution and build towards it. 3) How to modify solutions in the future to be more friendly and clear 4) The immense potential in agriculture to find solutions that impact millions of people in our country
What's next for Justice Society of Learning
Learn from our experience and to build upon it to improve ourselves so that we as a team may do our job to contribute towards the development and well being of the country.
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