Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis

Faruk Özel; Fatma Feyza Akyol; Ayhan İstanbullu

doi:10.35377/saucis...1613324

EN

Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis

Abstract

The agricultural sector increasingly relies on advanced technologies to enhance productivity and address challenges in disease management. In this context, deep learning-based image processing techniques play a crucial role in detecting diseases in tomato fruits. The aim of this research is to evaluate the performance of the YOLOv8 model in agricultural disease detection by comparing it with the YOLOv5 model. The results show that YOLOv8 outperforms YOLOv5 in detecting diseased tomatoes with higher accuracy (98.0% vs. 97.2%), precision (97.5% vs. 96.8%), recall (98.5% vs. 97.6%), and F1 score (97.8% vs. 97.0%). YOLOv8 also has a shorter inference time (35 ms vs. 45 ms). In detailed performance comparisons by disease type, YOLOv8 demonstrated superior results, particularly in “Early Blight,” with 99.0% accuracy and a 98.8% F1 score. In conclusion, YOLOv8 offers significant advantages in performance, speed, and training time for agricultural disease detection. These strengths have the potential to boost productivity and minimize losses through early disease detection and intervention. Furthermore, this research highlights that the success of deep learning models heavily depends on the quality and quantity of labeled data and provides valuable insights for the future development of agricultural disease detection technologies.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Faruk Özel ^*
0009-0001-2043-0951
Türkiye

Fatma Feyza Akyol
0000-0001-5880-4472
Türkiye

Ayhan İstanbullu
0000-0002-7066-4238
Türkiye

Early Pub Date

June 30, 2025

Publication Date

June 30, 2025

Submission Date

January 6, 2025

Acceptance Date

May 5, 2025

Published in Issue

Year 2025 Volume: 8 Number: 2

DOI

https://doi.org/10.35377/saucis...1613324

IZ

https://izlik.org/JA56YA34ZZ

Cite

RIS / Bibtex

APA

Özel, F., Akyol, F. F., & İstanbullu, A. (2025). Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis. Sakarya University Journal of Computer and Information Sciences, 8(2), 346-357. https://doi.org/10.35377/saucis...1613324

AMA

1.Özel F, Akyol FF, İstanbullu A. Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis. SAUCIS. 2025;8(2):346-357. doi:10.35377/saucis.1613324

Chicago

Özel, Faruk, Fatma Feyza Akyol, and Ayhan İstanbullu. 2025. “Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis”. Sakarya University Journal of Computer and Information Sciences 8 (2): 346-57. https://doi.org/10.35377/saucis. 1613324.

EndNote

Özel F, Akyol FF, İstanbullu A (June 1, 2025) Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis. Sakarya University Journal of Computer and Information Sciences 8 2 346–357.

IEEE

[1]F. Özel, F. F. Akyol, and A. İstanbullu, “Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis”, SAUCIS, vol. 8, no. 2, pp. 346–357, June 2025, doi: 10.35377/saucis...1613324.

ISNAD

Özel, Faruk - Akyol, Fatma Feyza - İstanbullu, Ayhan. “Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 346-357. https://doi.org/10.35377/saucis. 1613324.

JAMA

1.Özel F, Akyol FF, İstanbullu A. Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis. SAUCIS. 2025;8:346–357.

MLA

Özel, Faruk, et al. “Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, June 2025, pp. 346-57, doi:10.35377/saucis. 1613324.

Vancouver

1.Faruk Özel, Fatma Feyza Akyol, Ayhan İstanbullu. Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis. SAUCIS. 2025 Jun. 1;8(2):346-57. doi:10.35377/saucis. 1613324