Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction

• Published in: Ecological informatics 2024-12, Vol.84, p.102886, Article 102886
• Main Authors: Jeong, Seungtaek, Ko, Jonghan, Ban, Jong-oh, Shin, Taehwan, Yeom, Jong-min
• Format: Article
• Language: English
• Subjects: Crop model; Deep learning; Remote sensing; Rice yield
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2024.102886

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. [Display omitted] •Introduced a new crop modeling method integrating deep learning and remote sensing.•Developed and assessed deep neural network models to improve rice yield predictions.•Achieved Nash–Sutcliffe model efficiencies ranging from 0.954 to 0.996.•Identified performance variations, underscoring the need for diverse training data.•Combined cutting-edge computational techniques with established methods.