@inproceedings{Bansal2023,
 title = {A {{Deep Learning Model}} for {{Heterogeneous Dataset Analysis}} - {{Application}} to {{Winter Wheat Crop Yield Prediction}}},
 booktitle = {Information, Communication and Computing Technology},
 author = {Bansal, Yogesh and Lillis, David and Kechadi, M.-Tahar},
 editor = {Abawajy, Jemal and Tavares, Joao and Kharb, Latika and Chahal, Deepak and Nassif, Ali Bou},
 year = {2023},
 pages = {182--194},
 publisher = {{Springer Nature Switzerland}},
 address = {{Cham}},
 doi = {10.1007/978-3-031-43838-7_14},
 abstract = {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 reports that they perform better than traditional Machine Learning (ML) models. However, the existing LSTM cannot handle heterogeneous datasets (a combination of data that 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 outperformed the existing ML models.},
 isbn = {978-3-031-43838-7},
}