  Western countries rely heavily on wheat, and yield prediction is crucial.
Time-series deep learning models, such as Long Short Term Memory (LSTM), have
already been explored and applied to yield prediction. Existing literature
reported that they perform better than traditional Machine Learning (ML)
models. However, the existing LSTM cannot handle heterogeneous datasets (a
combination of data which varies and remains static with time). In this paper,
we propose an efficient deep learning model that can deal with heterogeneous
datasets. We developed the system architecture and applied it to the real-world
dataset in the digital agriculture area. We showed that it outperforms the
existing ML models.
