ARCHIVES
Year 2026 · Volume 6 · Issue 4
Original Article
Assessing Feature Contribution in Crop Yield Prediction Using Data Ablation and Explainable Artificial Intelligence Techniques
Dr Haruna Abdulrahman Enoch1
Peter Ogedebe2
1 Department of Computer Science, Nile University Abuja, Nigeria. 2 Professor, Department of Computer Science, Baze University Abuja, Nigeria.
Published Online: July-August 2026
Pages: 171-178
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260604021
References
[1]. Adewopo, J., Solano-Hermosilla, G., Colen, L., & Micale, F. (2024). Artificial intelligence applications in precision agriculture: A review
of machine learning techniques for crop yield prediction. Agricultural Systems, 219, 103943.
[2]. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B., &
Li, Z. (2019). A high-performance machine learning approach for crop yield prediction using satellite and climate data. Remote Sensing
of Environment, 232, 111245.
[3]. Chang, J., Wang, Y., Liu, H., Zhang, L., & Chen, Z. (2024). Hybrid deep neural networks with multi-task learning for rice yield prediction
using remote sensing data. Computers and Electronics in Agriculture, 216, 108613.
[4]. Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems (pp. 1–15). Springer.
[5]. Khaki, S., & Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10, 621.
[6]. Khan, A., Rahman, M., & Islam, S. (2023). Explainable artificial intelligence in smart agriculture: A review of interpretable machine
learning approaches. Artificial Intelligence in Agriculture, 7, 45–61.[7]. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
[8]. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing
Systems, 30, 4765–4774.
[9]. Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review.
Computers and Electronics in Agriculture, 177, 105709.
[10]. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer.
[11]. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.
[12]. You, J., Li, X., Low, M., Lobell, D., & Ermon, S. (2017). Deep Gaussian process for crop yield prediction based on remote sensing data.
In Proceedings of the AAAI Conference on Artificial Intelligence.
of machine learning techniques for crop yield prediction. Agricultural Systems, 219, 103943.
[2]. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B., &
Li, Z. (2019). A high-performance machine learning approach for crop yield prediction using satellite and climate data. Remote Sensing
of Environment, 232, 111245.
[3]. Chang, J., Wang, Y., Liu, H., Zhang, L., & Chen, Z. (2024). Hybrid deep neural networks with multi-task learning for rice yield prediction
using remote sensing data. Computers and Electronics in Agriculture, 216, 108613.
[4]. Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems (pp. 1–15). Springer.
[5]. Khaki, S., & Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10, 621.
[6]. Khan, A., Rahman, M., & Islam, S. (2023). Explainable artificial intelligence in smart agriculture: A review of interpretable machine
learning approaches. Artificial Intelligence in Agriculture, 7, 45–61.[7]. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
[8]. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing
Systems, 30, 4765–4774.
[9]. Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review.
Computers and Electronics in Agriculture, 177, 105709.
[10]. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer.
[11]. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.
[12]. You, J., Li, X., Low, M., Lobell, D., & Ermon, S. (2017). Deep Gaussian process for crop yield prediction based on remote sensing data.
In Proceedings of the AAAI Conference on Artificial Intelligence.
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