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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.
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Classification of Phalaenopsis plantlet parts and identification of suitable grasping point for automatic transplanting using machine vision.

机译：蝴蝶兰植株部分的分类以及使用机器视觉识别自动移植的合适抓点。

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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。该算法使用小植株骨骼的节点生成切割线，以将小植株分成其组成的叶和根。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取点。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，所提出的分类器在识别完整PTCP植株上的合适抓握点时可达到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的潜力，可以实现机器视觉自动植物移植自动系统。

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