Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize

Abstract

In this project we are using machine learning and deep
learning algorithms to predict future crop yield based
on weather data such as temperature and rainfall. If
farmers know the crop yield before sowing based on
historical weather data, then he may take better
decision. So, by employing machine/deep learning
algorithms we can inform farmers about future crop
yield. In proposed method we are using Irish Maize and
Potato yield dataset to train all machine learning
models and then these models can be used to predict
future crop yield. In proposed method we are using
random forest, SVR, DNN, CNN, ANN and LSTM. So,
we have implemented all 6 algorithms on both datasets.
To evaluate performance of each algorithm we are
calculating MSE and R2 Score where MSE refers to
mean square error (difference between TEST crop yield
and predicted yield). R2 refers to correct prediction
rate. So, for any algorithm MSE must be lower and R2
must be higher for better crop yield prediction.