TY  - JOUR
T1  - Disease Detection in Tomato Fruit Using Deep Learning Algorithms: Comparative Analysis
AU  - Özel, Faruk
AU  - Akyol, Fatma Feyza
AU  - İstanbullu, Ayhan
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.35377/saucis...1613324
JF  - Sakarya University Journal of Computer and Information Sciences
JO  - SAUCIS
PB  - Sakarya University
WT  - DergiPark
SN  - 2636-8129
SP  - 346
EP  - 357
VL  - 8
IS  - 2
LA  - en
AB  - 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.
KW  - Deep Learning
KW  - Object Detection
KW  - Image Processing
KW  - Tomato Disease
CR  - 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)
CR  - 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
CR  - 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.
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CR  - 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.
CR  - 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
CR  - 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
CR  - 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
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CR  - 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
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CR  - Gad, A. F. Evaluating Object Detection Models Using Mean Average Precision (mAP) Blog. 2021 https://blog.paperspace.com/mean-average-precision
UR  - https://doi.org/10.35377/saucis...1613324
L1  - https://dergipark.org.tr/en/download/article-file/4492747
ER  -