الگوریتم‌های یادگیری ماشین در پیش‌بینی عملکرد غلات: یک مرور تحلیلی

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Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R. & Chatila, R. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, pp.82-115. https://doi.org/10.1016/j.inffus.2019.12.012De Clercq, D., & Mahdi, A. 2025. Modern computational approaches for rice yield prediction: A systematic review of statistical and machine learning-based methods. Computers and Electronics in Agriculture, 231, 109852. https://doi.org/10.1016/j.compag.2024.109852

de Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. 2016. Mean absolute percentage error for regression models. Neurocomputing, 192, 38-48. https://doi.org/10.1016/j.neucom.2015.12.114

Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010

Hastie, T., Tibshirani, R., & Friedman, J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer, New York, NY. https://doi.org/10.1007/978-0-387-84858-7

Jabed, M.A., & Murad, M.A.A. 2024. Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability. Heliyon, 10(24): e40836. https://doi.org/10.1016/j.heliyon.2024.e40836

Kamilaris, A. & Prenafeta-Boldú, F.X. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016

Khaki, S., & Wang, L. 2019. Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10, 621. https://doi.org/10.3389/fpls.2019.00621

Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., & Liu, H. 2017. Feature selection: A data perspective. ACM Computing Surveys, 50(6), 94. https://doi.org/10.1145/3136625

Liakos, K.G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. 2018. Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674

Mohan, R. N. V. J., Rayanoothala, P. S., and Sree, R. P. 2025. Next-gen agriculture: Integrating AI and XAI for precision crop yield predictions. Frontiers in Plant Science, 15, 1451607. https://doi.org/10.3389/fpls.2024.1451607

Mohapatra, S.K., Asmare, B., Kekeba, K., & Deferisha, A.D. 2025. Cereal crop yield prediction using machine learning techniques. In: Singh, S., Sood, V., Srivastav, A.L., & Ampatzidis, Y. (Eds.), Hyperautomation in Precision Agriculture (pp. 97–112). Academic Press. ISBN: 9780443241390.

Montesinos-López, O.A., Crespo-Herrera, L., Pierre, C.S., Cano-Paez.0, B., Huerta-Prado, G.I., Mosqueda-González, B.A., Ramos-Pulido, S., Gerard, G., Alnowibet, K., Fritsche-Neto, R., Montesinos-López, A. & Crossa, J. 2024. Feature engineering of environmental covariates improves plant genomic-enabled prediction. Frontiers in Plant Science, 15, 1349569. https://doi.org/10.3389/fpls.2024.1349569

Nazari, L., & Zinati, Z. 2023. Gene expression classification for biomarker identification in maize subjected to various biotic stresses. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(3), 2170-2176. https://doi.org/10.1109/TCBB.2022.3233844

Nazari, L., Aslan, M.F., Sabanci, K., & Ropelewska, E. 2023. Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress. Scientific Reports, 13(1), 1-12. https://doi.org/10.1038/s41598-023-42984-4

Nazari, L., Khazaei, A., & Ropelewska, E. 2022. Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning. Cereal Chemistry, 99(4), 843-849. https://doi.org/10.1002/cche.10542

Nazari, L., Shaker, M., Karimi, A., & Ropelewska, E. 2021. Identification of sorghum genotypes using a machine vision system. Journal of Food Process Engineering, 44 (5), e13673. https://doi.org/10.1111/jfpe.13673

Okupska, E., Gozdowski, D., Pudełko, R., & Wójcik-Gront, E. 2025. Cereal and rapeseed yield forecast in Poland at regional level using machine learning and classical statistical models. Agriculture, 15(9), 984. https://doi.org/10.3390/agriculture15090984

Pudjihartono, N., Fadason, T., Kempa-Liehr, A.W. & O’Sullivan, J.M. 2022. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics, 2:927312. https://doi.org/10.3389/fbinf.2022.927312

Ropelewska, E. & Nazari, L. 2021. The effect of drought stress of sorghum grains on the textural features evaluated using machine learning. European Food Research and Technology, 247, 2787-2798. https://doi.org/10.1007/s00217-021-03832-9

Sokolova, M., & Lapalme, G. 2009. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437. https://doi.org/10.1016/j.ipm.2009.03.002

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. https://doi.org/10.1016/j.compag.2020.105709

Zeinalzadeh-Tabrizi, H., & Nazari, L. 2025. Machine learning-based transcriptome mining to discover key genes for density stress in sweet corn. Ecological Genetics and Genomics, 35(100349), 1-17. https://doi.org/10.1016/j.egg.2025.100349