Abstract
Considering crop yield prediction as critical to optimizing agricultural practices and food security, this question is critical to U.S. agricultural planning and regional food security; relevant research on corn, one of the essential crops, must focus on the accurate methods for predicting this crop. It has been discussed that yield prediction models generally rely on simplistic approaches, which fail to capture complex, non-linear relationships in agricultural data. This work fills the knowledge gap by making use of advanced machine-learning techniques to improve the accuracy of corn yield prediction. This study focuses on county-level regional forecasting(U.S) to support agricultural policy and supply chain planning rather than field-specific management decisions. The methodology is in line with the Special Section on Sustainable Computing for Next-Generation Low-Carbon Agricultural Consumer Electronics by designing a data-efficient algorithm that focuses on the Random Forest Classifier, Gradient Boosting Classifier, and Ensemble Voting Classifier. The development of this model entailed the pre-processing of historical data concerning corn yield, defining pertinent attributes, and assessing the confusion matrix, ROC curve, and SHAP values for explainability. This work proposes an ensemble model which has achieved remarkable accuracy and robustness, excelling in performance relative to the existing approaches. The model has also made solid predictions, with a precision, recall, and F1-score of 0.92 and a training accuracy of 0.97. The SHAP further enhances transparency into the features that drive predictions, hence making the model more interpretable. This is of great importance to agricultural planning; this would most probably offer a sound instrument to predict corn yield and optimize resources in agricultural consumer practices. This paper strongly advocates energy-efficient algorithm design, intelligent applications, sustainable computing, efficiency, environmental impact, and resource recycling to drive toward sustainable and efficient corn yield prediction.
Data availability
The datasets used and analysed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/guillemservera/grains-and-cereals-futures
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Acknowledgments
This project was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2026R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. We also appreciate technical insights and collaborative support provided by the VLCMatrix Lab during this research.
Funding
This research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2026R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
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T. Kehkashan conceptualized the study, designed the methodology, and drafted the main manuscript. M. Abdelhaq and A. S. Al-Shamayleh contributed to the development of the predictive model and the interpretation of results. A. Akhunzada provided technical oversight, contributed to refining the manuscript, and supervised the overall research direction. S. Z. Alharthi contributed to the literature review and data curation. M. Hamza, and M.A.Khan supported data preprocessing, experiment setup, and results visualization under supervision. All authors reviewed and approved the final version of the manuscript.
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Kehkashan, T., Abdelhaq, M., Al-Shamayleh, A.S. et al. Sustainable regional corn yield prediction for the United States through interpretable machine learning approach. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43213-4
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DOI: https://doi.org/10.1038/s41598-026-43213-4
