Hybrid Deep Learning-based Models for Crop Yield Prediction

Alexandros Oikonomidis, Cagatay Catal*, Ayalew Kassahun

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

41 Citations (Scopus)


Predicting crop yield is a complex task since it depends on multiple factors. Although many models have been developed so far in the literature, the performance of current models is not satisfactory, and hence, they must be improved. In this study, we developed deep learning-based models to evaluate how the underlying algorithms perform with respect to different performance criteria. The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN)-Deep Neural Networks (DNN), CNN-XGBoost, CNN-Recurrent Neural Networks (RNN), and CNN-Long Short Term Memory (LSTM). For the case study, we performed experiments on a public soybean dataset that consists of 395 features including weather and soil parameters and 25,345 samples. The results showed that the hybrid CNN-DNN model outperforms other models, having an RMSE equal to 0.266, an MSE of 0.071, and an MAE of 0.199. The predictions of the model fit with an R2 of 0.87. The second-best result was achieved by the XGBoost model, which required less time to execute compared to the other DL-based models.
Original languageEnglish
Pages (from-to)1-18
JournalApplied artificial intelligence
Issue number1
Early online date22 Jan 2022
Publication statusPublished - 31 Dec 2022


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