Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection

Authors

  • Ecem Iren Izmir Kavram Vocational School, Turkiye
Volume: 14 | Issue: 2 | Pages: 13714-13719 | April 2024 | https://doi.org/10.48084/etasr.7033

Abstract

Deep learning is a concept of artificial neural networks and a subset of machine learning. It deals with algorithms that train and process datasets to make inferences for future samples, imitating the human process of learning from experiences. In this study, the YOLOv5 and YOLOv6 object detection models were compared on a plant dataset in terms of accuracy and time metrics. Each model was trained to obtain specific results in terms of mean Average Precision (mAP) and training time. There was no considerable difference in mAP between both models, as their results were close. YOLOv5, having 63.5% mAP, slightly outperformed YOLOv6, while YOLOv6, having 49.6% mAP50-95, was better in detection than YOLOv5. Furthermore, YOLOv5 trained data in a shorter time than YOLOv6, since it has fewer parameters.

Keywords:

deep learning, YOLOv5, YOLOv6, plant leaf disease detection, convolutional neural network

Downloads

Download data is not yet available.

References

L. Christiaensen, Z. J. Rutledge, and J. E. Taylor, "The Future of Work in Agriculture : Some Reflections," The World Bank, Policy Research Working Paper 9193, Mar. 2020.

S. Nigam and R. Jain, "Plant disease identification using Deep Learning: A review," Indian Journal of Agricultural Sciences, vol. 90, no. 2, pp. 249–257, Mar. 2020.

R. Rajamohanan and B. C. Latha, "An Optimized YOLO v5 Model for Tomato Leaf Disease Classification with Field Dataset," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12033–12038, Dec. 2023.

N. Shelar, S. Shinde, S. Sawant, S. Dhumal, and K. Fakir, "Plant Disease Detection Using Cnn," ITM Web of Conferences, vol. 44, 2022, Art. no. 03049.

V. Singh and A. K. Misra, "Detection of plant leaf diseases using image segmentation and soft computing techniques," Information Processing in Agriculture, vol. 4, no. 1, pp. 41–49, Mar. 2017.

C. Janiesch, P. Zschech, and K. Heinrich, "Machine learning and deep learning," Electronic Markets, vol. 31, no. 3, pp. 685–695, Sep. 2021.

J. F. Mas and J. J. Flores, "The application of artificial neural networks to the analysis of remotely sensed data," International Journal of Remote Sensing, vol. 29, no. 3, pp. 617–663, Feb. 2008.

J. Kaur and W. Singh, "Tools, techniques, datasets and application areas for object detection in an image: a review," Multimedia Tools and Applications, vol. 81, no. 27, pp. 38297–38351, Nov. 2022.

M. Carranza-García, J. Torres-Mateo, P. Lara-Benítez, and J. García-Gutiérrez, "On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data," Remote Sensing, vol. 13, no. 1, Jan. 2021, Art. no. 89.

S. Norkobil Saydirasulovich, A. Abdusalomov, M. K. Jamil, R. Nasimov, D. Kozhamzharova, and Y.-I. Cho, "A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments," Sensors, vol. 23, no. 6, Jan. 2023, Art. no. 3161.

M. R. M. Ismat Saira Gillani1, "Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey," CS & IT Conference Proceedings, vol. 12, no. 16, Sep. 2022.

O. Kivrak and M. Z. Gürbüz, "Performance Comparison of YOLOv3,YOLOv4 and YOLOv5 algorithms : A Case Study for Poultry Recognition," Avrupa Bilim ve Teknoloji Dergisi, no. 38, pp. 392–397, Aug. 2022.

I. P. Sary, S. Andromeda, and E. U. Armin, "Performance Comparison of YOLOv5 and YOLOv8 Architectures in Human Detection using Aerial Images," Ultima Computing : Jurnal Sistem Komputer, vol. 15, no. 1, pp. 8–13, Jun. 2023.

F. Zhou, H. Zhao, and Z. Nie, "Safety Helmet Detection Based on YOLOv5," in 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China, Jan. 2021, pp. 6–11.

X. Yuan et al., "Performance Comparison of Sea Cucumber Detection by the Yolov5 and DETR Approach," Journal of Marine Science and Engineering, vol. 11, no. 11, Nov. 2023, Art. no. 2043.

T. Saidani, "Deep Learning Approach: YOLOv5-based Custom Object Detection," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12158–12163, Dec. 2023.

D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, "PlantDoc: A Dataset for Visual Plant Disease Detection," in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Hyderabad, India, Jan. 2020, pp. 249–253.

O. E. Olorunshola, M. E. Irhebhude, and A. E. Evwiekpaefe, "A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms," Journal of Computing and Social Informatics, vol. 2, no. 1, pp. 1–12, Feb. 2023.

H. K. Jung and G. S. Choi, "Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions," Applied Sciences, vol. 12, no. 14, Jan. 2022, Art. no. 7255.

T. Saidani, R. Ghodhbani, A. Alhomoud, A. Alshammari, H. Zayani, and M. B. Ammar, "Hardware Acceleration for Object Detection using YOLOv5 Deep Learning Algorithm on Xilinx Zynq FPGA Platform," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 13066–13071, Feb. 2024.

N. Aburaed, M. Alsaad, S. A. Mansoori, and H. Al-Ahmad, "A Study on the Autonomous Detection of Impact Craters," in Artificial Neural Networks in Pattern Recognition, Dubai, United Arab Emirates, 2023, pp. 181–194.

C. Li et al., "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications." arXiv, Sep. 07, 2022.

T. Y. Lin et al., "Microsoft COCO: Common Objects in Context," in Computer Vision – ECCV 2014, Zurich, Switzerland, 2014, pp. 740–755.

T. Bakirman, "An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery," International Journal of Environment and Geoinformatics, vol. 10, no. 1, pp. 9–15, Mar. 2023.

N. I. M. Yusof, A. Sophian, H. F. M. Zaki, A. A. Bawono, A. H. Embong, and A. Ashraf, "Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study," Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, pp. 350–360, Feb. 2024.

M. Horvat, L. Jelečević, and G. Gledec, "Comparative Analysis of YOLOv5 and YOLOv6 Models Performance for Object Classification on Open Infrastructure: Insights and Recommendations," presented at the 34th Central European Conference on Information and Intelligent Systems, Zagreb, Croatia, 2023, pp. 317–324.

Downloads

How to Cite

[1]
Iren, E. 2024. Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection. Engineering, Technology & Applied Science Research. 14, 2 (Apr. 2024), 13714–13719. DOI:https://doi.org/10.48084/etasr.7033.

Metrics

Abstract Views: 549
PDF Downloads: 504

Metrics Information