Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods

Alican Doğan; Cemal Yüksel

doi:10.35377/saucis...1626178

EN

Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods

Abstract

It is very important in agriculture to detect diseases in plants and recovery solutions to produce more crop and to improve efficiency. Enhancements in automated disease detection and analysis can offer significant advantages for taking prompt action, enabling interventions at earlier stages to treat the disease and prevent its spread. This proactive approach could help minimize damage to crop yields. This research is aimed at improving classification performance for apple plant leaf disease detection using transfer learning approaches. The goal is to take necessary precautions for unhealthy apple plants for productive agriculture and healthy food. It discriminates sick apple plants from healthy counterparts by implementing image processing with apple leaf photographs. In this study, traditional machine learning methods are applied for apple plant disease detection task and the classification achievement scores are maximized with transfer learning techniques. The experiments are conducted on a real-world data set including 3164 apple leaf images. As a result, those experiments reveal that transfer learning methods especially EfficientNetB0 has made a significant improvement on classification accuracy for this task. Accuracy and F-score values obtained by transfer learning methods are over 99% which states that they can be considered reliable for plant disease detection tasks.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Alican Doğan ^*
0000-0002-0553-2888
Türkiye

Cemal Yüksel
0000-0003-2722-5114
Türkiye

Early Pub Date

October 13, 2025

Publication Date

December 29, 2025

Submission Date

January 24, 2025

Acceptance Date

September 1, 2025

Published in Issue

Year 2025 Volume: 8 Number: 4

DOI

https://doi.org/10.35377/saucis...1626178

IZ

https://izlik.org/JA53MG79KC

Cite

RIS / Bibtex

APA

Doğan, A., & Yüksel, C. (2025). Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. Sakarya University Journal of Computer and Information Sciences, 8(4), 592-605. https://doi.org/10.35377/saucis...1626178

AMA

1.Doğan A, Yüksel C. Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. SAUCIS. 2025;8(4):592-605. doi:10.35377/saucis.1626178

Chicago

Doğan, Alican, and Cemal Yüksel. 2025. “Enchancing Apple Plant Leaf Disease Detection Performance With Transfer Learning Methods”. Sakarya University Journal of Computer and Information Sciences 8 (4): 592-605. https://doi.org/10.35377/saucis. 1626178.

EndNote

Doğan A, Yüksel C (December 1, 2025) Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. Sakarya University Journal of Computer and Information Sciences 8 4 592–605.

IEEE

[1]A. Doğan and C. Yüksel, “Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods”, SAUCIS, vol. 8, no. 4, pp. 592–605, Dec. 2025, doi: 10.35377/saucis...1626178.

ISNAD

Doğan, Alican - Yüksel, Cemal. “Enchancing Apple Plant Leaf Disease Detection Performance With Transfer Learning Methods”. Sakarya University Journal of Computer and Information Sciences 8/4 (December 1, 2025): 592-605. https://doi.org/10.35377/saucis. 1626178.

JAMA

1.Doğan A, Yüksel C. Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. SAUCIS. 2025;8:592–605.

MLA

Doğan, Alican, and Cemal Yüksel. “Enchancing Apple Plant Leaf Disease Detection Performance With Transfer Learning Methods”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 4, Dec. 2025, pp. 592-05, doi:10.35377/saucis. 1626178.

Vancouver

1.Alican Doğan, Cemal Yüksel. Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. SAUCIS. 2025 Dec. 1;8(4):592-605. doi:10.35377/saucis. 1626178