| Autor: |
Seungtaek Jeong, Jonghan Ko, Jong-oh Ban, Taehwan Shin, Jong-min Yeom |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Ecological Informatics, Vol 84, Iss , Pp 102886- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1574-9541 |
| DOI: |
10.1016/j.ecoinf.2024.102886 |
| Popis: |
This study introduces a novel crop modeling approach based on cutting-edge computational tools to advance crop production monitoring methodologies, and, thereby, tackle global food security issues. Our approach pioneers integrating deep learning and remote sensing with process-based crop models to enhance rice yield predictions while leveraging the strengths and weaknesses of each model. We developed and evaluated four models based on distinct deep neural network architectures: feed-forward neural network, long short-term memory (LSTM), gated recurrent units, and bidirectional LSTM. All the models demonstrated high predictive accuracies, with percent biases of 0.74–2.62 and Nash–Sutcliffe model efficiencies of 0.954–0.996; however, the LSTM performed best among the four models. Notably, the models' performances varied when applied to regional datasets that were not included in the training phase; this highlighted the critical need for diverse training data to enhance model robustness. This research marks a significant advancement in agricultural modeling by combining state-of-the-art computational techniques with established methodologies, setting a new standard for crop yield prediction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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