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远程果检测使用计算机视觉和基于机器学习的电子陷.

Miguel Molina-Rotger1, Alejandro Morán1, Miguel Angel Miranda2,3

  • 1Industrial Engineering and Construction Department, University of the Balearic Islands, Palma, Spain.

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概括

这项研究引入了一种使用机器学习 (ML) 的电子陷,用于精确检测橄. 结合随机森林和支持矢量机算法,实现了高精度,有助于在精准农业中可持续的害虫管理.

关键词:
计算机视觉 计算机视觉边缘计算是一种边缘计算.机器学习是机器学习.橄果果害虫害害虫害虫害害虫害害虫害害虫害虫害害害虫害虫害害害虫害虫害害虫害害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害虫害精准农业 精准农业 精准农业随机的森林随机的森林远程传感是一种遥感技术.支持矢量机器的支持矢量机器.

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 精准农业需要用于害虫管理的智能监测系统.
  • 计算机视觉和人工智能对于早期发现害虫至关重要,比如橄.
  • 有限的数据可用性给最先进的深度学习技术带来了挑战.

研究的目的:

  • 检查使用随机森林 (RF) 和支持矢量机 (SVM) 算法检测和分类的橄.
  • 将这些算法实现在由树Pi B+板供电的电子陷系统中.
  • 通过机器学习解决害虫检测中的数据稀缺问题.

主要方法:

  • 利用随机森林 (RF) 和支持矢量机 (SVM) 算法进行图像分类.
  • 开发了一个电子陷系统,其中包含一个Raspberry Pi B+板,用于实时数据收集.
  • 研究了RF和SVM的联合应用,以提高在有限的培训数据下对分类的准确性.

主要成果:

  • 结合RF-SVM方法实现了89.1%的精度,用于橄的检测.
  • 单个算法性能显示SVM的准确率为94.5%,RF的准确率为91.9%,在区分物种与其他昆虫时.
  • 在小型物联网设备上成功展示了基于ML的图像分类的可行性.

结论:

  • 该研究成功实施了一种基于机器学习的电子陷,用于有效检测橄.
  • 使用小型物联网设备进行图像分类,为资源优化和隐私提供了巨大的潜力.
  • 通过网络陷的可扩展性保证了持续的数据采集和更高的准确性,以实现可持续的种群管理.