A Deep-Learning-Based Model for the Detection of Diseased Tomato Leaves
Author:
Abdullah Akram1, Amran Gehad Abdullah2ORCID, Tahmid S. M. Ahanaf1, Alabrah Amerah3ORCID, AL-Bakhrani Ali A.4ORCID, Ali Abdulaziz5
Affiliation:
1. School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China 2. Department of Management Science and Engineering, Dalian University of Technology, Dalian 116024, China 3. Department of Information Systems, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia 4. College of Software Engineering, Dalian University of Technology, Dalian 116024, China 5. School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Abstract
This study introduces a You Only Look Once (YOLO) model for detecting diseases in tomato leaves, utilizing YOLOV8s as the underlying framework. The tomato leaf images, both healthy and diseased, were obtained from the Plant Village dataset. These images were then enhanced, implemented, and trained using YOLOV8s using the Ultralytics Hub. The Ultralytics Hub provides an optimal setting for training YOLOV8 and YOLOV5 models. The YAML file was carefully programmed to identify sick leaves. The results of the detection demonstrate the resilience and efficiency of the YOLOV8s model in accurately recognizing unhealthy tomato leaves, surpassing the performance of both the YOLOV5 and Faster R-CNN models. The results indicate that YOLOV8s attained the highest mean average precision (mAP) of 92.5%, surpassing YOLOV5’s 89.1% and Faster R-CNN’s 77.5%. In addition, the YOLOV8s model is considerably smaller and demonstrates a significantly faster inference speed. The YOLOV8s model has a significantly superior frame rate, reaching 121.5 FPS, in contrast to YOLOV5’s 102.7 FPS and Faster R-CNN’s 11 FPS. This illustrates the lack of real-time detection capability in Faster R-CNN, whereas YOLOV5 is comparatively less efficient than YOLOV8s in meeting these needs. Overall, the results demonstrate that the YOLOV8s model is more efficient than the other models examined in this study for object detection.
Funder
King Saud University, Riyadh, Saudi Arabia
Reference29 articles.
1. Hung, J., Goodman, A., and Ravel, D. (2020). Keras R-CNN: Library for cell detection in biological images using deep neural networks. BMC Bioinform., 21. 2. A classification–detection approach of COVID-19 based on chest X-ray and CT by using Keras pre-trained deep learning models;Deng;Comput. Model. Eng. Sci.,2020 3. Pachipala, Y., Harika, M., Aakanksha, B., and Kavitha, M. (2022, January 16–18). Object Detection using TensorFlow. Proceedings of the International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India. 4. Object Detection for Autonomous Vehicle Using TensorFlow;Pandian;Advances in Intelligent Systems and Computing, Proceedings of the Intelligent Computing, Information and Control Systems, ICICCS 2019, Secunderabad, India, 27–28 June 2019,2020 5. Yue, Z., Xue, Y., Gefan, Z., Jiabao, W., Yanyi, L., Liping, H., Xue, J., Xingzhao, L., Junchi, Y., and Chengqi, L. (2022, January 10–14). Mmrotate: A rotated object detection benchmark using Pytorch. Proceedings of the 30th ACM International Conference on Multimedia, Lisbon, Portugal.
|
|