Evaluation of YOLOv8 Model Series with HOP for Object Detection in Complex Agriculture Domains

Abstract

In recent years, many studies have been conducted in-depth investigating YOLO Models for object detection in the field of agriculture. For this reason, this study focused on four datasets containing different agricultural scenarios, and 20 dif-ferent trainings were carried out with the objectives of understanding the detec-tion capabilities of YOLOv8 and HPO (optimization of hyperparameters). While Weed/Crop and Pineapple datasets reached the most accurate measurements with YOLOv8n in mAP score of 0.8507 and 0.9466 respectively, the prominent model for Grapes and Pear datasets was YOLOv8l in mAP score of 0.6510 and 0.9641. This situation shows that multiple-species or in different developmental stages of a single species object YOLO training highlights YOLOv8n, while only object detection extracting from background scenario naturally highlights YOLOv8l Model.

Keywords

• YOLOv8 state-of-the-art networks
• hyperparameter optimization
• agricultural images
• object detection.

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