Logo PTI Logo FedCSIS

Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Multi-level Optimization-based Ensemble Machine Learning for Efficient Crop Yield Prediction in Saudi Arabia

,

DOI: http://dx.doi.org/10.15439/2025F5652

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 429–440 ()

Full text

Abstract. Accurate crop yield prediction is essential for enhancing food security, optimizing resource use, and supporting smart farming initiatives. Traditional statistical models often fail to capture the nonlinear interactions between environmental, climatic, and agronomic variables. To address these challenges, this research evaluated seven ensemble machine learning algorithms: Random Forest, Bagged Decision Tree, AdaBoost, Gradient Boosting, Stochastic Gradient Boosting, eXtreme Gradient Boosting, and CatBoost. Being interested in the region of Saudi Arabia, we collected and integrated multi-source data that resulted in meteorological factors (temperature, humidity, precipitation), vegetation indices, pesticide usage, and historical yield records of thirty eight crop categories. While selecting only eight crops to study in the optimization phase, each model was assessed under four experimental configurations: baseline models, hyperparameter tuning, outliers' removal, and Bayesian optimization. Results showed that the optimized models performed up to 47\\% better than the default models. More precisely, hyperparameter tuning showed marginal gains, and Bayesian optimization and outliers' removal led to noticeable performance improvements. However, the effectiveness of each strategy was crop-specific.

References

  1. M. Ashfaq, I. Khan, A. Alzahrani, M. U. Tariq, H. Khan, and A. Ghani, Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion, IEEE Access, vol. 12, pp. 40947–40961, 2024. https://dx.doi.org/10.1109/ACCESS.2024.3376735.
  2. A. D. Yewlea, L. Mirzayeva, and O. Karakuş, Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction, Preprint submitted to Elsevier, Feb. 2025.
  3. A. Joshi, B. Pradhan, S. Chakraborty, R. Varatharajoo, S. Gite, and A. Alamri, Deep-Transfer-Learning Strategies for Crop Yield Prediction Using Climate Records and Satellite Image Time-Series Data, Remote Sensing, vol. 16, no. 24, 4804, 2024. https://doi.org/10.3390/rs16244804.
  4. Q. M. Ilyas, M. Ahmad, and A. Mehmood, Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies, Bioengineering, vol. 10, no. 2, article 125, pp. 1–24, 2023. https://dx.doi.org/10.3390/bioengineering10020125.
  5. P. Brandt, F. Beyer, P. Borrmann, M. Möller, and H. Gerighausen, Ensemble learning-based crop yield estimation: a scalable approach for supporting agricultural statistics, GIScience & Remote Sensing, vol. 61, no. 1, pp. 2367808, 2024. DOI: 10.1080/15481603.2024.2367808.
  6. Y. Wang, Q. Zhang, F. Yu, N. Zhang, X. Zhang, Y. Li, M. Wang, and J. Zhang, Progress in Research on Deep Learning-Based Crop Yield Prediction, Agronomy, vol. 14, no. 10, article 2264, pp. 1–26, 2024. https://dx.doi.org/10.3390/agronomy14102264.
  7. J. Ansarifar, L. Wang, and S. V. Archontoulis, An Interaction Regression Model for Crop Yield Prediction, Scientific Reports, vol. 11, article 17754, 2021. https://dx.doi.org/10.1038/s41598-021-97221-7.
  8. M. Rashid, B. S. Bari, Y. Yusup, M. A. Kamaruddin, and N. Khan, A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction, IEEE Access, vol. 9, pp. 63406–63439, 2021.
  9. M. H. Al-Adhaileh and T. H. H. Aldhyani, Artificial Intelligence Framework for Modeling and Predicting Crop Yield to Enhance Food Security in Saudi Arabia, PeerJ Computer Science, vol. 8, article e1104, 2022. https://dx.doi.org/10.7717/peerj-cs.1104.
  10. F. M. Talaat, Crop Yield Prediction Algorithm (CYPA) in Precision Agriculture Based on Internet of Things (IoT) Techniques and Climate Changes, Neural Computing and Applications, vol. 35, pp. 17281–17292, 2023. https://dx.doi.org/10.1007/s00521-023-08619-5.
  11. M. S. Rao, A. Singh, N. V. S. Reddy, and D. U. Acharya, Crop Prediction Using Machine Learning, Journal of Physics: Conference Series, vol. 2161, 012033, 2022. https://dx.doi.org/10.1088/1742-6596/2161/1/012033.
  12. H. F. Assous, H. AL-Najjar, N. Al-Rousan, and D. AL-Najjar, Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries, Sustainability, vol. 15, no. 12, article 9392, 2023. https://dx.doi.org/10.3390/su15129392.
  13. K. Palanivel and C. Surianarayanan, An Approach for Prediction of Crop Yield Using Machine Learning and Big Data Techniques, International Journal of Computer Engineering and Technology (IJCET), vol. 10, no. 3, pp. 110–118, 2019.
  14. M. M. Islam, M. Alharthi, R. S. Alkadi, R. Islam, and A. K. M. Masum, Crop Yield Prediction through Machine Learning: A Path Towards Sustainable Agriculture and Climate Resilience in Saudi Arabia, AIMS Agriculture and Food, vol. 9, no. 4, pp. 980–1003, 2024. https://dx.doi.org/10.3934/agrfood.2024053.
  15. A. Morales and F. J. Villalobos, Using Machine Learning for Crop Yield Prediction in the Past or the Future, Frontiers in Plant Science, vol. 14, article 1128388, 2023. https://dx.doi.org/10.3389/fpls.2023.1128388.
  16. S. V. Joshua, A. S. M. Priyadharson, R. Kannadasan, A. A. Khan, W. Lawanont, F. A. Khan, A. U. Rehman, and M. J. Ali, Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum, Computers, Materials and Continua, vol. 72, no. 3, pp. 5663–5679, 2022. https://dx.doi.org/10.32604/cmc.2022.027178.
  17. C. Trentin, Y. Ampatzidis, C. Lacerda, and L. Shiratsuchi, Tree Crop Yield Estimation and Prediction Using Remote Sensing and Machine Learning: A Systematic Review, Smart Agricultural Technology, vol. 9, article 100556, 2024. https://dx.doi.org/10.1016/j.atech.2024.100556.
  18. T. van Klompenburg, A. Kassahun, and C. Catal, Crop yield prediction using machine learning: A systematic literature review, Computers and Electronics in Agriculture, vol. 177, p. 105709, 2020.
  19. K. Jhajharia, P. Mathur, S. Jain, and S. Nijhawan, Crop Yield Prediction using Machine Learning and Deep Learning Techniques, Procedia Computer Science, vol. 218, pp. 406–417, 2023.
  20. Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms, Chapman & Hall/CRC Machine Learning & Pattern Recognition Series, CRC Press, 2012.
  21. E.-S. M. El-Kenawy, A. A. Alhussan, N. Khodadadi, S. Mirjalili, and M. M. Eid, Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture, Potato Research, vol. 68, pp. 759–792, 2025. https://dx.doi.org/10.1007/s11540-024-09753-w.
  22. B. Bischl, M. Binder, P. Lang, T. Pfisterer, and A. Richter, Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 12785–12810, 2023. https://dx.doi.org/10.1109/TPAMI.2023.3243326.
  23. G. Pang, C. Shen, L. Cao, and A. van den Hengel, Deep Learning for Anomaly Detection: A Review, ACM Computing Surveys, vol. 54, no. 10, Article 200, pp. 1–38, Jan. 2020. https://dx.doi.org/10.1145/3439950.
  24. J. Snoek, H. Larochelle, and R. P. Adams, Practical Bayesian Optimization of Machine Learning Algorithms, Advances in Neural Information Processing Systems (NeurIPS), vol. 25, pp. 2951–2959, 2012.
  25. K. A. Al-Gaadi, R. Madugundu, E. Tola, and S. El-Hendawy, Prediction of Potato Crop Yield Using Precision Agriculture Techniques in the Eastern Region of Saudi Arabia, Journal of the Saudi Society of Agricultural Sciences, vol. 15, no. 2, pp. 82–89, 2016.
  26. K. A. Al-Gaadi, R. Madugundu, E. Tola, S. El-Hendawy, and S. Marey, Satellite-Based Determination of the Water Footprint of Carrots and Onions Grown in the Arid Climate of Saudi Arabia, Remote Sensing, vol. 14, no. 23, pp. 1–20, 2022.
  27. T. Li, O. M. L. Valencia, K. Johansen, and M. F. McCabe, A Retrospective Analysis of National-Scale Agricultural Development in Saudi Arabia from 1990 to 2021, Remote Sensing, vol. 15, no. 3, pp. 1–21, 2023.
  28. S. Ahmed, A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron, Sustainability, vol. 15, no. 4, pp. 3017, 2023.
  29. A. Jabbari, A. Humayed, F. A. Reegu, M. Uddin, Y. Gulzar, and M. Majid, Smart Farming Revolution: Farmer’s Perception and Adoption of Smart IoT Technologies for Crop Health Monitoring and Yield Prediction in Jizan, Saudi Arabia, Sustainability, vol. 15, no. 19, pp. 14541, 2023, https://dx.doi.org/10.3390/su151914541.
  30. https://www.fao.org/faostat/en/home, last accessed on May 2025.
  31. https://climateknowledgeportal.worldbank.org/, last accessed on May 2025.
  32. G. Ignesti, D. Moroni, M. Martinelli, Towards the actual deployment of robust, adaptable, and maintainable AI models for sustainable agriculture, Position Papers of the 19thConference on Computer Science and Intelligence Systems (FedCSIS), 2024, https://dx.doi.org/10.15439/2024F2991.
  33. Z. Sbai, Deep Learning Models and Their Ensembles for Robust Agricultural Yield Prediction in Saudi Arabia, Sustainability, vol. 17, no. 13, pp. 1-26, 2025, https://dx.doi.org/10.3390/su17135807.