Crop Recommendation

Inspiration

We wanted to build a project that has real-life applications, one of the largest impacts of climate change is crop failure. It is very important to realise that land is a limited resource and we need to use it efficiently hence, this project is built to help determine farmers find the most suitable crop to grow.

What it does

Inputs are taken for certain parameters (Temperature, humidity, pH, rainfall, NPK) and are run through the neural network which recommends the best crop to grow given the conditions inputted.

How we built it

We chose a Kaggle dataset and explored the data and made graphs. We built a neural network and trained the model with data. Inputs are taken for certain parameters and an output of the best crop is given. The code is written in Python

Challenges we ran into

We are from a different timezone and hence had a much shorter time period to complete the project because of this we had to cut down on some plans we had for the project. We had bugs throughout the project which we eventually ironed out

Accomplishments that we're proud of

The model has high accuracy and efficiency and the project has a real-world use case that benefits humanity

What we learned

We expanded on our knowledge of neural networks and have learned plotly, a library used to plot graphs better. We also gained time management skills from this competition