Improved Tomato Disease Detection with YOLOv5 and YOLOv8

Authors

  • Rabie Ahmed Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia | Department of Mathematics and Computer Science, Faculty of Science, Beni-Suef University, Egypt
  • Eman H. Abd-Elkawy Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia | Department of Mathematics and Computer Science, Faculty of Science, Beni-Suef University, Egypt
Volume: 14 | Issue: 3 | Pages: 13922-13928 | June 2024 | https://doi.org/10.48084/etasr.7262

Abstract

This study delves into the application of deep learning for precise tomato disease detection, focusing on four crucial categories: healthy, blossom end rot, splitting rotation, and sun-scaled rotation. The performance of two lightweight object detection models, namely YOLOv5l and YOLOv8l, was compared on a custom tomato disease dataset. Initially, both models were trained without data augmentation to establish a baseline. Subsequently, diverse data augmentation techniques were obtained from Roboflow to significantly expand and enrich the dataset content. These techniques aimed to enhance the models' robustness to variations in lighting, pose, and background conditions. Following data augmentation, the YOLOv5l and YOLOv8l models were re-trained and their performance across all disease categories was meticulously analyzed. After data augmentation, a significant improvement in accuracy was observed for both models, highlighting its effectiveness in bolstering the models' ability to accurately detect tomato diseases. YOLOv8l consistently achieved slightly higher accuracy compared to YOLOv5l, particularly when excluding background images from the evaluation.

Keywords:

tomato disease detection, Roboflow, YOLOv8, YOLOv5, accuracy

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References

Y. Gulzar, "Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique," Sustainability, vol. 15, no. 3, Jan. 2023, Art. no. 1906.

M. Afonso et al., "Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning," Frontiers in Plant Science, vol. 11, Nov. 2020.

G. Moreira, S. A. Magalhães, T. Pinho, F. N. dos Santos, and M. Cunha, "Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato," Agronomy, vol. 12, no. 2, Feb. 2022, Art. no. 356.

Y. Mu, T.-S. Chen, S. Ninomiya, and W. Guo, "Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques," Sensors, vol. 20, no. 10, Jan. 2020, Art. no. 2984.

S. A. Magalhães et al., "Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse," Sensors, vol. 21, no. 10, Jan. 2021, Art. no. 3569.

F. Zeng, Y. Liu, Y. Ye, J. Zhou, and X. Liu, "A detection method of Edge Coherent Mode based on improved SSD," Fusion Engineering and Design, vol. 179, Jun. 2022, Art. no. 113141.

H. Peng et al., "General improved SSD model for picking object recognition of multiple fruits in natural environment.," Transactions of the Chinese Society of Agricultural Engineering, vol. 34, no. 16, pp. 155–162, 2018.

P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, "A Review of Yolo Algorithm Developments," Procedia Computer Science, vol. 199, pp. 1066–1073, Jan. 2022.

J. Wu, Z. Kuang, L. Wang, W. Zhang, and G. Wu, "Context-Aware RCNN: A Baseline for Action Detection in Videos," in Computer Vision – ECCV 2020, Glasgow, UK, 2020, pp. 440–456.

G. Liu, J. C. Nouaze, P. L. Touko Mbouembe, and J. H. Kim, "YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3," Sensors, vol. 20, no. 7, Jan. 2020, Art. no. 2145.

B. Hu and J. Wang, "Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network," IEEE Access, vol. 8, pp. 108335–108345, 2020.

Y. Yang, J. Li, J. Nie, S. Yang, and J. Tang, "Cotton Stubble Detection Based on Improved YOLOv3," Agronomy, vol. 13, no. 5, May 2023, Art. no. 1271.

R. Gai, N. Chen, and H. Yuan, "A detection algorithm for cherry fruits based on the improved YOLO-v4 model," Neural Computing and Applications, vol. 35, no. 19, pp. 13895–13906, Jul. 2023.

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.

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.

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

J. Zhou, Y. Zhang, and J. Wang, "RDE-YOLOv7: An Improved Model Based on YOLOv7 for Better Performance in Detecting Dragon Fruits," Agronomy, vol. 13, no. 4, Apr. 2023, Art. no. 1042.

N. C. Eli-Chukwu, "Applications of Artificial Intelligence in Agriculture: A Review," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4377–4383, Aug. 2019.

A. Abbas, U. Maqsood, S. U. Rehman, K. Mahmood, T. AlSaedi, and M. Kundi, "An Artificial Intelligence Framework for Disease Detection in Potato Plants," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12628–12635, Feb. 2024.

S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, "Disease Detection in Apple Leaves Using Image Processing Techniques," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, Apr. 2022.

S. L. Sanga, D. Machuve, and K. Jomanga, "Mobile-based Deep Learning Models for Banana Disease Detection," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5674–5677, Jun. 2020.

L. Loyani and D. Machuve, "A Deep Learning-based Mobile Application for Segmenting Tuta Absoluta’s Damage on Tomato Plants," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7730–7737, Oct. 2021.

M. J. A. Soeb et al., "Tea leaf disease detection and identification based on YOLOv7 (YOLO-T)," Scientific Reports, vol. 13, no. 1, Apr. 2023, Art. no. 6078.

G. Yang, J. Wang, Z. Nie, H. Yang, and S. Yu, "A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention," Agronomy, vol. 13, no. 7, Jul. 2023, Art. no. 1824.

Q. H. Phan, V. T. Nguyen, C. H. Lien, T. P. Duong, M. T. K. Hou, and N. B. Le, "Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models," Plants, vol. 12, no. 4, Jan. 2023, Art. no. 790.

"deseaseTomato Image Dataset." Feb. 17, 2024, [Online]. Available: https://universe.roboflow.com/datasetsnbu/deseasetomato/dataset/8.

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How to Cite

[1]
R. Ahmed and E. H. Abd-Elkawy, “Improved Tomato Disease Detection with YOLOv5 and YOLOv8”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 13922–13928, Jun. 2024.

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