• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Engineering in Agriculture >Classification of Phalaenopsis Plantlet Parts and Identification of Suitable Grasping Point for Automatic Transplanting Using Machine Vision
【24h】

Classification of Phalaenopsis Plantlet Parts and Identification of Suitable Grasping Point for Automatic Transplanting Using Machine Vision

机译：蝴蝶兰小植物部位的分类和使用机器视觉识别自动移植的合适抓点

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study develops an image-processing algorithm to segment and classify the components of Phalaenopsis tissue culture plantlets (PTCPs) and to determine a suitable grasping location on the roots for an automatic transplanting operation. The algorithm uses the nodes of the plantlet's skeleton to generate cutting lines to separate the plantlet into its constituent leaves and roots. A Bayes classifier based on an optimal combination of color and shape features is then applied to classify the individual segments of the plantlet as either "leaf" or "root" segments. The root segment with the highest decision value based on the Bayes theorem is then selected, and the midpoint of its skeleton specified as a suitable grasping point for an automatic transplanting operation. The classification results obtained by the Bayes classifier for manually cut samples demonstrate that a classification accuracy of 99.9% is achievable given an appropriate choice of color and shape features. Implementing the optimal set of features, the proposed classifier achieves a 94.9% success rate in identifying suitable grasping points on complete PTCP plantlets. Therefore, the experimental results indicate that the proposed image-processing algorithm has the potential for integration with a robotic handling device to realize an automatic machine vision plantlet transplanting system.
机译：这项研究开发了一种图像处理算法，对蝴蝶兰组织培养小植株（PTCP）的成分进行分割和分类，并确定根部上适合自动移植操作的合适位置。该算法使用小植物骨骼的节点生成切割线，以将小植物分离为其组成的叶和根。然后将基于颜色和形状特征的最佳组合的贝叶斯分类器应用于将小植株的各个片段分类为“叶”或“根”片段。然后根据贝叶斯定理选择决策值最高的根段，并将其骨架的中点指定为自动移植操作的合适抓点。由贝叶斯分类器获得的用于手动切割样品的分类结果表明，如果适当选择颜色和形状特征，则可以达到99.9％的分类精度。通过实现最佳功能集，该分类器在识别完整PTCP植株上的合适抓握点时达到94.9％的成功率。因此，实验结果表明，所提出的图像处理算法具有与机器人操纵装置集成以实现自动机器视觉小植株移植系统的潜力。

著录项

  • 来源
    《Applied Engineering in Agriculture》 |2008年第1期|p.89-99|共11页
  • 作者

    Y. J. Huang F.-F. Lee;

  • 作者单位

    The authors are Ying-Jen Huang, ASABE Student Member, Graduate Student, Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung, Taiwan and Instructor, Department of Biomechatronic Engineering, National Chiayi University, Chiayi, Taiwan;

    and Fang-Fan Lee, Professor, Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung, Taiwan. Corresponding author: Fang-Fan Lee, Dept. of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, 250, Kuo Kuang Rd., Taichung 402, Taiwan;

    phone: 886-4-22859039;

    fax: 886-4-22879351;

    e-mail: fflee@dragon.nchu.edu.tw.;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Phalaenopsis plantlet , Machine vision, Feature selection.;

    机译：蝴蝶兰植株;机器视觉;特征选择。;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号