Abstract: 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. Keywords: citrus, machine vision, citrus harvesting robot (CHR), branch, identification, multi-class support vector machine (SVM) DOI: 10.3965/j.ijabe.20140702.014 Citation: Lü Q, Cai J R, Liu B, Deng L, Zhang Y J. Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine. Int J Agric & Biol Eng, 2014; 7(2): 115－121.
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机译：【摘要】随着农业劳动力的减少和生产成本的增加，近年来柑桔收获机器人的研究越来越受到人们的关注。为了成功实现机器人收割和机器人安全，识别成熟的柑橘类水果和障碍物是机器人收割的首要任务。在这项工作中，开发了由彩色CCD相机和计算机组成的机器视觉系统来完成这些任务。在晴朗和多云的条件下拍摄柑橘树的图像。由于水果和树枝的亮度和位置随机性不同，这些图像中对象的红色，绿色和蓝色值发生了巨大变化。传统的阈值分割不足以解决这些问题。通过形态学运算成功的多类支持向量机（SVM）被用来同时分割水果和树枝。柑橘类水果的识别率为92.4％，并且可以识别直径大于5个像素的分支。结果表明，该算法可用于检测CHR的果实和分支。关键词：柑橘，机器视觉，柑橘收获机器人（CHR），分支机构，识别，多类支持向量机（SVM）DOI：10.3965 / j.ijabe.20140702.014引用：吕庆，蔡建荣，刘乙，邓L， Zhang Y J.使用机器视觉和支持向量机识别柑橘收获机器人自然场景中的水果和树枝。农业与生物工程学杂志，2014; 7（2）：115－121。
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