Yolo-pest:一个优化的YoloV8x用于使用智能陷检测小昆虫害虫
Ayesha Hakim1,2, Amit Kumar Srivastava3,4, Ali Hamza5
1Institute of Computing, MNS - University of Agriculture, Multan, 60000, Pakistan. Ayesha.hakim@mnsuam.edu.pk.
Scientific reports
|April 23, 2025
概括
这项研究引入了一个物联网智能陷用于自动化昆虫害虫监测,使用微调的YOLOv8x模型在识别果和秋季军方面达到94%的准确性.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 果和秋季军虫是重要的农业害虫,影响水果和作物产量.
- 传统的害虫监测方法是劳动密集型的,需要经常访问农场.
- 成年害虫的移动性使手动现场监测复杂化.
研究的目的:
- 开发和评估一种自动化系统,用于监测开放场地和室内环境中的昆虫害虫.
- 微调YOLOv8x模型以准确实时检测果和秋季军虫.
- 将物联网技术集成到远程虫害数据收集和分析中.
主要方法:
- 基于物联网的智能陷的开发,配备了费洛蒙吸引剂和摄像头.
- 微调YOLOv8x深度学习模型 (Yolo-pest) 以对目标害虫进行对象检测.
- 在干旱条件下在果果园和玉米田中部署和测试智能陷系统.
- 使用灰度和彩色图像进行验证,以确保稳定性.
主要成果:
- 物联网智能陷系统实现了果和秋季军虫的平均检测准确率为94%.
- 微调的YOLOv8x模型在不同的图像类型和环境中表现出一致的性能.
- 该系统能够实时,远程识别害虫,并为农民提供数据可访问性.
结论:
- 开发的物联网智能陷系统为自动化昆虫害虫监测提供了高效和准确的解决方案.
- 微调的YOLOv8x显著提高了农业应用中的害虫检测能力.
- 这项技术使农民能够及时获得有效的害虫控制策略的信息.
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