Research Article
BibTex RIS Cite

Year 2025, Volume: 8 Issue: 2, 346 - 357, 30.06.2025

Faruk Özel , Fatma Feyza Akyol , Ayhan İstanbullu

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

Abstract

References

  • Domates Hastalık ve Zararlıları ile Mücadele, T.C. Tarım ve Orman Bakanlığı Gıda ve Kontrol Genel Müdürlüğü Bitki Sağlığı ve Karantina Daire Başkanlığı. 2021. s. 5. (Türkçe)
  • Chowdhury, M. E. H., Rahman, T., Khandakar, A., Ibtehaz, N., Khan, A. U., et al. Tomato Leaf Diseases Detection Using Deep Learning Technique. Technology in Agriculture. 2021. DOI: 10.5772/intechopen.97319
  • S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. G. Sophia and B. Pavithra, "Tomato Leaf Disease Detection Using Deep Learning Techniques," 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2020, pp. 979-983, doi: 10.1109/ICCES48766.2020.9137986.
  • Li D, Yin Z, Zhao Y, Zhao W, Li J. MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization. Agriculture. 2023; 13(6):1140. https://doi.org/10.3390/agriculture13061140
  • Liu Y, Hu Y, Cai W, Zhou G, Zhan J, Li L. DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification. Comput Intell Neurosci. 2022 Apr 15;2022:4848425. doi: 10.1155/2022/4848425. PMID: 35463291; PMCID: PMC9033327.
  • Lacotte V, Peignier S, Raynal M, Demeaux I, Delmotte F, da Silva P. Spatial–Spectral Analysis of Hyperspectral Images Reveals Early Detection of Downy Mildew on Grapevine Leaves. International Journal of Molecular Sciences. 2022; 23(17):10012. https://doi.org/10.3390/ijms231710012
  • Yue, X., Qi, K., Na, X., Zhang, Y., Liu, Y., et al.. Improved YOLOv8-Seg network for instance segmentation of healthy and diseased tomato plants in the growth stage. Agriculture, 2023; 13(8), 1643. https://doi.org/10.3390/agriculture13081643
  • Zheng, H., Wang, G. ve Li, X. YOLOX-Dense-CT: YOLOX ve DenseNet'e dayalı kiraz domatesleri için bir algılama algoritması. Gıda Tedbiri 16 , 4788–4799 (2022). https://doi.org/10.1007/s11694-022-01553-5
  • Kim T, Lee D-H, Kim K-C, Choi T, Yu JM. Tomato Maturity Estimation Using Deep Neural Network. Applied Sciences. 2023; 13(1):412. https://doi.org/10.3390/app13010412
  • Ge Y, Lin S, Zhang Y, Li Z, Cheng H, Dong J, Shao S, Zhang J, Qi X, Wu Z. Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot. Machines. 2022; 10(6):489. https://doi.org/10.3390/machines10060489
  • Liu G, Mao S, Kim JH. A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis. Sensors. 2019; 19(9):2023. https://doi.org/10.3390/s19092023
  • Yang G, Wang J, Nie Z, Yang H, Yu S. A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention. Agronomy. 2023; 13(7):1824. https://doi.org/10.3390/agronomy13071824
  • Rubanga, D. P., Loyani, L., Richard, M., & Shimada, S. A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants. Inside: International Conference on Learning Representations 2020 Workshop on Computer Vision for Agriculture; Tokyo University of Agriculture; The Nelson Mandela African Institute of Science and Technology. 2020; https://doi.org/10.48550/arXiv.2004.04023
  • J. Redmon and A. Farhadi, "YOLOV3: an incremental improvement," in IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, 2018; s. 1-6, https://doi.org/10.48550/arXiv.1804.02767
  • Sakin, M. Discover the Power of YOLOv8: Next Generation Object Detection Algorithm. 2023.
  • Tan, M., and Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning 2019; s. 6105-6114. https://doi.org/10.48550/arXiv.1905.11946
  • Gad, A. F. Evaluating Object Detection Models Using Mean Average Precision (mAP) Blog. 2021 https://blog.paperspace.com/mean-average-precision

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

Year 2025, Volume: 8 Issue: 2, 346 - 357, 30.06.2025

Faruk Özel , Fatma Feyza Akyol , Ayhan İstanbullu

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

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

Deep Learning Object Detection Image Processing Tomato Disease

References

  • Domates Hastalık ve Zararlıları ile Mücadele, T.C. Tarım ve Orman Bakanlığı Gıda ve Kontrol Genel Müdürlüğü Bitki Sağlığı ve Karantina Daire Başkanlığı. 2021. s. 5. (Türkçe)
  • Chowdhury, M. E. H., Rahman, T., Khandakar, A., Ibtehaz, N., Khan, A. U., et al. Tomato Leaf Diseases Detection Using Deep Learning Technique. Technology in Agriculture. 2021. DOI: 10.5772/intechopen.97319
  • S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. G. Sophia and B. Pavithra, "Tomato Leaf Disease Detection Using Deep Learning Techniques," 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2020, pp. 979-983, doi: 10.1109/ICCES48766.2020.9137986.
  • Li D, Yin Z, Zhao Y, Zhao W, Li J. MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization. Agriculture. 2023; 13(6):1140. https://doi.org/10.3390/agriculture13061140
  • Liu Y, Hu Y, Cai W, Zhou G, Zhan J, Li L. DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification. Comput Intell Neurosci. 2022 Apr 15;2022:4848425. doi: 10.1155/2022/4848425. PMID: 35463291; PMCID: PMC9033327.
  • Lacotte V, Peignier S, Raynal M, Demeaux I, Delmotte F, da Silva P. Spatial–Spectral Analysis of Hyperspectral Images Reveals Early Detection of Downy Mildew on Grapevine Leaves. International Journal of Molecular Sciences. 2022; 23(17):10012. https://doi.org/10.3390/ijms231710012
  • Yue, X., Qi, K., Na, X., Zhang, Y., Liu, Y., et al.. Improved YOLOv8-Seg network for instance segmentation of healthy and diseased tomato plants in the growth stage. Agriculture, 2023; 13(8), 1643. https://doi.org/10.3390/agriculture13081643
  • Zheng, H., Wang, G. ve Li, X. YOLOX-Dense-CT: YOLOX ve DenseNet'e dayalı kiraz domatesleri için bir algılama algoritması. Gıda Tedbiri 16 , 4788–4799 (2022). https://doi.org/10.1007/s11694-022-01553-5
  • Kim T, Lee D-H, Kim K-C, Choi T, Yu JM. Tomato Maturity Estimation Using Deep Neural Network. Applied Sciences. 2023; 13(1):412. https://doi.org/10.3390/app13010412
  • Ge Y, Lin S, Zhang Y, Li Z, Cheng H, Dong J, Shao S, Zhang J, Qi X, Wu Z. Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot. Machines. 2022; 10(6):489. https://doi.org/10.3390/machines10060489
  • Liu G, Mao S, Kim JH. A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis. Sensors. 2019; 19(9):2023. https://doi.org/10.3390/s19092023
  • Yang G, Wang J, Nie Z, Yang H, Yu S. A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention. Agronomy. 2023; 13(7):1824. https://doi.org/10.3390/agronomy13071824
  • Rubanga, D. P., Loyani, L., Richard, M., & Shimada, S. A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants. Inside: International Conference on Learning Representations 2020 Workshop on Computer Vision for Agriculture; Tokyo University of Agriculture; The Nelson Mandela African Institute of Science and Technology. 2020; https://doi.org/10.48550/arXiv.2004.04023
  • J. Redmon and A. Farhadi, "YOLOV3: an incremental improvement," in IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, 2018; s. 1-6, https://doi.org/10.48550/arXiv.1804.02767
  • Sakin, M. Discover the Power of YOLOv8: Next Generation Object Detection Algorithm. 2023.
  • Tan, M., and Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning 2019; s. 6105-6114. https://doi.org/10.48550/arXiv.1905.11946
  • Gad, A. F. Evaluating Object Detection Models Using Mean Average Precision (mAP) Blog. 2021 https://blog.paperspace.com/mean-average-precision
There are 17 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Faruk Özel 0009-0001-2043-0951

Fatma Feyza Akyol 0000-0001-5880-4472

Ayhan İstanbullu 0000-0002-7066-4238

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 2025Volume: 8 Issue: 2

Cite

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 Özel F, Akyol FF, İstanbullu A. Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis. SAUCIS. June 2025;8(2):346-357. doi:10.35377/saucis.1613324
Chicago Özel, Faruk, Fatma Feyza Akyol, and Ayhan İstanbullu. “Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis”. Sakarya University Journal of Computer and Information Sciences 8, no. 2 (June 2025): 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 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, 2025, doi: 10.35377/saucis...1613324.
ISNAD Özel, Faruk et al. “Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 2025), 346-357. https://doi.org/10.35377/saucis. 1613324.
JAMA 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, 2025, pp. 346-57, doi:10.35377/saucis. 1613324.
Vancouver Özel F, Akyol FF, İstanbullu A. Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis. SAUCIS. 2025;8(2):346-57.
 Download Cover Image


INDEXING & ABSTRACTING & ARCHIVING


 31045 31044  31046 31047 

31043 28939 28938 34240


29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License