Predict end-of-season wheat and maize yields for each grid cell and year, using the daily weather experienced during the growing season (and just before), along with the soil texture and ambient CO2 level. The challenge is to predict yields in the future - from 2021 to 2100 - under a high-emissions climate change scenario, using training data from the last few decades (1980 - 2020). The prediction of yield is carried out using Python- Tendsorflow, and appilied methods like Random Forest, FeedForward Neural Networks, Long Short Term Memory (LSTM) model.
