Classification of cherry maturity stages using machine learning methods

Nurullah Doğan; Fatih Özyurt; İnanç Özgen

doi:10.31015/jaefs.2026.1.1

Classification of cherry maturity stages using machine learning methods

Abstract

Accurate and rapid determination of the maturity level of agricultural products is of great importance for identifying the appropriate harvest time, preserving product quality, and minimizing economic losses. In particular, in fruits with high export potential such as cherries, determining the maturity stage accurately is a critical process for both producers and consumers. In this study, a machine learning-based method is proposed for classifying different maturity stages of cherries. Within the scope of the study, cherries belonging to five different maturity stages were collected from cherry orchards in Elazığ province, and a total of 3000 high-resolution images were obtained. The images were subjected to preprocessing steps, followed by feature extraction, and a dataset was constructed for classification. Ten different machine learning algorithms were employed in the experiments. These algorithms include Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), XGBoost, Logistic Regression, Ridge Classifier, LightGBM, CatBoost, Random Forest, Extra Trees, and K-Nearest Neighbors (KNN). The models were evaluated both without optimization and with an Optuna-based optimization process. Experimental findings demonstrated that optimization significantly improved classification performance. Among the models, the SVM achieved the highest accuracy rate compared to the others. The accuracy value obtained without optimization was 93.33%, while this value increased to 95.16% after optimization. In addition, other machine learning methods also achieved high accuracy rates, and in particular, some models showed a significant improvement in classification success after optimization. The results indicate that machine learning-based approaches are highly effective in classifying cherry maturity stages. These methods are considered to contribute to the improvement of quality control, harvest planning, and the development of intelligent decision support systems in agricultural production. Furthermore, the findings of this study are expected to shed light on future research to be conducted on different fruit types and to support digitalization processes in agricultural production.

Keywords

Artificial intelligence, machine learning, image processing, data fusion, precision agriculture

Supporting Institution

Fırat University and TUBİTAK

Project Number

This research was supported by the Scientific Research Projects Unit of Fırat University (Project No: MF.24.88) and by TUBİTAK under Project No: 123O399

Ethical Statement

Ethical approval was not required for this study.

Thanks

The authors would like to thank the Scientific Research Projects Unit of Fırat University and TUBİTAK for their financial support.

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Details

Primary Language

English

Subjects

Environmental Assessment and Monitoring, Agricultural Systems Analysis and Modelling

Journal Section

Research Article

Authors

Nurullah Doğan ^*
0009-0002-4714-5402
Türkiye

Fatih Özyurt
0000-0002-8154-6691
Türkiye

İnanç Özgen
Türkiye

Early Pub Date

February 15, 2026

Publication Date

February 15, 2026

Submission Date

November 12, 2025

Acceptance Date

December 23, 2025

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.31015/jaefs.2026.1.1

IZ

https://izlik.org/JA25HF53RL

APA

Doğan, N., Özyurt, F., & Özgen, İ. (2026). Classification of cherry maturity stages using machine learning methods. International Journal of Agriculture Environment and Food Sciences, 10(1), 1-13. https://doi.org/10.31015/jaefs.2026.1.1

AMA

1.Doğan N, Özyurt F, Özgen İ. Classification of cherry maturity stages using machine learning methods. int. j. agric. environ. food sci. 2026;10(1):1-13. doi:10.31015/jaefs.2026.1.1

Chicago

Doğan, Nurullah, Fatih Özyurt, and İnanç Özgen. 2026. “Classification of Cherry Maturity Stages Using Machine Learning Methods”. International Journal of Agriculture Environment and Food Sciences 10 (1): 1-13. https://doi.org/10.31015/jaefs.2026.1.1.

EndNote

Doğan N, Özyurt F, Özgen İ (March 1, 2026) Classification of cherry maturity stages using machine learning methods. International Journal of Agriculture Environment and Food Sciences 10 1 1–13.

IEEE

[1]N. Doğan, F. Özyurt, and İ. Özgen, “Classification of cherry maturity stages using machine learning methods”, int. j. agric. environ. food sci., vol. 10, no. 1, pp. 1–13, Mar. 2026, doi: 10.31015/jaefs.2026.1.1.

ISNAD

Doğan, Nurullah - Özyurt, Fatih - Özgen, İnanç. “Classification of Cherry Maturity Stages Using Machine Learning Methods”. International Journal of Agriculture Environment and Food Sciences 10/1 (March 1, 2026): 1-13. https://doi.org/10.31015/jaefs.2026.1.1.

JAMA

1.Doğan N, Özyurt F, Özgen İ. Classification of cherry maturity stages using machine learning methods. int. j. agric. environ. food sci. 2026;10:1–13.

MLA

Doğan, Nurullah, et al. “Classification of Cherry Maturity Stages Using Machine Learning Methods”. International Journal of Agriculture Environment and Food Sciences, vol. 10, no. 1, Mar. 2026, pp. 1-13, doi:10.31015/jaefs.2026.1.1.

Vancouver

1.Nurullah Doğan, Fatih Özyurt, İnanç Özgen. Classification of cherry maturity stages using machine learning methods. int. j. agric. environ. food sci. 2026 Mar. 1;10(1):1-13. doi:10.31015/jaefs.2026.1.1

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Classification of cherry maturity stages using machine learning methods

Classification of cherry maturity stages using machine learning methods

Abstract

Keywords

Supporting Institution

Project Number

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Abstracting & Indexing Services

© International Journal of Agriculture, Environment and Food Sciences