Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine
Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine
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DOI：
10.3965/j.ijabe.20140702.014
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发表时间：
2014-04-01
影响因子：
2.4
通讯作者：
Zhang Yajing
中科院分区：
文献类型：
--
作者：
Qiang, Lu;Cai Jianrong;Zhang Yajing
随着农业劳动力的减少和生产成本的提高，柑橘收获机器人的研究近年来受到越来越多的关注。为了机器人采摘的成功和机器人的安全，成熟柑橘果实和障碍物的识别是机器人采摘的首要任务。本研究以彩色CCD摄影机与电脑组成机器视觉系统，以完成上述任务。柑橘树的图像在晴天和多云条件下拍摄。由于果实和树枝的亮度和位置的随机性不同，这些图像中的物体的红色、绿色和蓝色值发生了显著变化。传统的阈值分割方法不能有效地解决这些问题。采用基于形态学运算的多类支持向量机（SVM）对果实和枝条进行同时分割。柑橘类水果的识别率为92.4%，且能识别直径大于5个像素的分支。实验结果表明，该算法可以有效地检测果实和枝条。
With the decrease of agricultural labor and the increase of production cost, the researches on citrus harvesting robot (CHR) have received more and more attention in recent years. For the success of robotic harvesting and the safety of robot, the identification of mature citrus fruit and obstacle is the priority of robotic harvesting. In this work, a machine vision system, which consisted of a color CCD camera and a computer, was developed to achieve these tasks. Images of citrus trees were captured under sunny and cloudy conditions. Due to varying degrees of lightness and position randomness of fruits and branches, red, green, and blue values of objects in these images are changed dramatically. The traditional threshold segmentation is not efficient to solve these problems. Multi-class support vector machine (SVM), which succeeds by morphological operation, was used to simultaneously segment the fruits and branches in this study. The recognition rate of citrus fruit was 92.4%, and the branch of which diameter was more than 5 pixels, could be recognized. The results showed that the algorithm could be used to detect the fruits and branches for CHR.