目标检测
计算机科学
帧速率
人工智能
帧(网络)
推论
深度学习
模式识别(心理学)
计算机视觉
机器学习
电信
作者
Akram Abdullah,Gehad Abdullah Amran,S. M. Ahanaf Tahmid,Amerah Alabrah,Ali A. AL-Bakhrani,Abdulaziz Ali
出处
期刊:Agronomy
[Multidisciplinary Digital Publishing Institute]
日期:2024-07-22
卷期号:14 (7): 1593-1593
被引量:3
标识
DOI:10.3390/agronomy14071593
摘要
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.
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