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Hybrid Deep Learning-based Models for Crop Yield Prediction.pdf (1.78 MB)

Hybrid Deep Learning-based Models for Crop Yield Prediction

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journal contribution
submitted on 2024-03-21, 06:27 and posted on 2024-03-21, 06:28 authored by Alexandros Oikonomidis, Cagatay Catal, Ayalew Kassahun

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.

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Published in: Applied Artificial Intelligence
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Open Access funding provided by the Qatar National Library.



  • English


Taylor & Francis

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU