基于人工智能的作物管理框架,用于使用视觉传感器数据检测害虫
Asma Khan1, Sharaf J Malebary2, L Minh Dang3
1Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea.
Plants (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了一种优化的YOLOv5s模型,用于基于无人机的农业害虫检测. 改进后的模型实现了高精度和回忆,彻底改变了作物监测和害虫管理.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 农作物疾病和害虫侵袭对农业生产率构成重大威胁.
- 传统的害虫检测方法往往是劳动密集型或计算昂贵的.
- 无人驾驶飞行器 (UAV) 技术为高效的农业监测提供了潜力.
研究的目的:
- 开发和评估一个优化的深度学习模型,用于基于无人机的农业害虫检测和分类.
- 与现有方法相比,提高害虫识别的准确性和效率.
- 通过先进的害虫管理策略,为可持续农业做出贡献.
主要方法:
- 用先进的注意力模块,扩展的CSP模块和精细的特征提取来修改YOLOv5s模型.
- 使用无人机在农业环境中获取空中数据.
- 培训和测试模型的数据集包括五种常见的农业害虫.
主要成果:
- 提议的优化YOLOv5s模型在害虫检测和分类方面表现出卓越的性能.
- 实现了平均精度96.0%,平均回忆率93.0%,平均平均精度 (mAP) 95.0%.
- 在实验测试中表现优于各种标准YOLOv5模型版本.
结论:
- 优化的YOLOv5s模型提供了一个精确而高效的解决方案,用于使用无人机实时检测害虫.
- 这项技术具有显著的潜力,可以在以无人机为中心的生态系统中增强农业生产和预防疾病.
- 该研究强调了将先进的深度学习与无人机集成到可持续的害虫管理中的有效性.
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