Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms

Gözde Alp; Fatih Soygazi

Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms

Abstract

AAs the agricultural landscape continues to evolve, the synergy between the crop recommendation and machine learning (ML) innovations holds the potential to revolutionize the way farmers make decisions, ultimately driving sustainable and efficient food production. This study investigates the efficacy of various machine learning algorithms for crop recommendation, a crucial aspect of precision agriculture. Six prominent algorithms are evaluated utilizing the Crop Recommendation Dataset, those are: Attentive Interpretable Tabular Learning, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors, Decision Tree, LightGBM, and Random Forest (RF). Synthetic Minority Oversampling Technique (SMOTE) is applied to each algorithm to increase the number of samples in the dataset. When datasets are small, models may not be able to learn enough features to classify problems effectively in real-time situations. Data augmentation, including techniques like SMOTE, appears as a way to surpass this limitation by increasing the amount of available data. Our results demonstrate the effectiveness of these algorithms in accurately predicting suitable crops based on environmental and soil parameters. Notably, XGBoost achieved an accuracy of 0.9909, while RF combined with SMOTE attained the highest accuracy of 0.9981. This superior performance is attributed to SMOTE’s ability to generate a balanced dataset by increasing the number of samples in each class based on the available data, thereby enhancing the model’s predictive capability for crop recommendation. The key contribution of this study is the demonstration that applying SMOTE for data augmentation can improve the predictive accuracy of various machine learning algorithms. This research underscores the potential of machine learning, particularly ensemble methods coupled with oversampling techniques, in driving data-driven agricultural practices for enhanced crop yield and resource optimization.

Keywords

References

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Details

Primary Language

English

Subjects

Computer Software, Software Engineering (Other)

Journal Section

Research Article

Authors

Gözde Alp
0000-0002-6479-3500
Türkiye

Fatih Soygazi ^*
0000-0001-8426-2283
Türkiye

Publication Date

October 8, 2025

Submission Date

April 23, 2025

Acceptance Date

July 27, 2025

Published in Issue

Year 2025 Volume: 9 Number: 4

IZ

https://izlik.org/JA52WU96RF

Cite

RIS / Bibtex

APA

Alp, G., & Soygazi, F. (2025). Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms. Turkish Journal of Engineering, 9(4), 612-620. https://izlik.org/JA52WU96RF

AMA

1.Alp G, Soygazi F. Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms. TUJE. 2025;9(4):612-620. https://izlik.org/JA52WU96RF

Chicago

Alp, Gözde, and Fatih Soygazi. 2025. “Enhancing Crop Recommendation With SMOTE-Augmented Machine Learning Algorithms”. Turkish Journal of Engineering 9 (4): 612-20. https://izlik.org/JA52WU96RF.

EndNote

Alp G, Soygazi F (October 1, 2025) Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms. Turkish Journal of Engineering 9 4 612–620.

IEEE

[1]G. Alp and F. Soygazi, “Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms”, TUJE, vol. 9, no. 4, pp. 612–620, Oct. 2025, [Online]. Available: https://izlik.org/JA52WU96RF

ISNAD

Alp, Gözde - Soygazi, Fatih. “Enhancing Crop Recommendation With SMOTE-Augmented Machine Learning Algorithms”. Turkish Journal of Engineering 9/4 (October 1, 2025): 612-620. https://izlik.org/JA52WU96RF.

JAMA

1.Alp G, Soygazi F. Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms. TUJE. 2025;9:612–620.

MLA

Alp, Gözde, and Fatih Soygazi. “Enhancing Crop Recommendation With SMOTE-Augmented Machine Learning Algorithms”. Turkish Journal of Engineering, vol. 9, no. 4, Oct. 2025, pp. 612-20, https://izlik.org/JA52WU96RF.

Vancouver

1.Gözde Alp, Fatih Soygazi. Enhancing Crop Recommendation with SMOTE-Augmented Machine Learning Algorithms. TUJE [Internet]. 2025 Oct. 1;9(4):612-20. Available from: https://izlik.org/JA52WU96RF