报告详情
Optimizing YOLOv8 for Efficient Tomato Recognition in Greenhouse Environments Using Drone Imagery
编号：34
访问权限：公开
更新：2024-08-05 15:25:24 浏览：996次
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摘要
This study delves into the practical application and fine-tuning of YOLOv8 models for real-time tomato recognition using drone imagery within greenhouse environments. We evaluated YOLO’s speed, robustness, and adaptability, finding that varying batch sizes and epochs had minimal impact on performance. Notably, YOLOv8n performed on par with the extra-large YOLOv8x model, offering a significant advantage: training time was up to 60 times shorter. Further tuning and innovative training strategies revealed that the Final Learning Rate (lrf) and dataset annotation quality were the most influential factors for model performance. Fine-tuning the lrf and re-annotating datasets markedly improved accuracy, underscoring the importance of optimizing learning rates and maintaining high-quality annotations for effective YOLO models. Our results also demonstrated the superiority of YOLOv8 over YOLOv5. The optimized YOLOv8n model is well-prepared for deployment in upcoming tomato recognition tasks, paving the way for more efficient agricultural monitoring. This work also provides valuable insights into the broader field of object recognition and offers practical guidance for researchers tackling similar challenges.
关键词
AI, Agriculture, CV, Deep Learning, Machine Learning
报告人
Oleg Shovkovyy
University Lecturer CMKL稿件作者
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重要日期
-
会议日期
10月24日
2024
至10月27日
2024
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10月14日 2024
初稿截稿日期
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10月29日 2024
注册截止日期
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10月31日 2024
报告提交截止日期
主办单位
国际科学联合会
IEEE泰国分会
IEEE计算机学会泰国分会
IEEE泰国分会
IEEE计算机学会泰国分会
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
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2025年12月29日 约旦 Zarqa
2025亚洲通信与网络会议
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