基于机器视觉的橘子分级系统关键技术  

Key Technology of Orange Grading System Based on Machine Vision

作　　者：胡璐萍 刘懂懂 刘通 王琪璇 陈荣荣 Hu Luping;Liu Dongdong;Liu Tong;Wang Qixuan;Chen Rongrong(College of Mechanical and Electrical Engineering,Xi'an Traffic Engineering Institute,Xi'an 710300,Shaanxi,China;School of Mechanical and Electrical Engineering,Xi'an Polytechnic University,Xi'an 710048,Shaanxi,China)

机构地区：[1]西安交通工程学院机械与电气工程学院,陕西西安710300 [2]西安工程大学机电工程学院,陕西西安710048

出　　处：《农业技术与装备》2023年第11期28-30,33,共4页Agricultural Technology & Equipment

基　　金：基于工业机器人视觉感知的钢轨表面质量检测技术研究(23JK0531)。

摘　　要：在橘子生产销售中,做好橘子分级工作十分重要,现有的半机械分级方法分级效率低,品质参差不齐。基于此,提出了一种基于机器视觉的橘子分级技术。该技术的主要步骤为:先采用灰度化将原始彩色图像的3通道转换成只有单一通道的灰度图,然后采用二值化对橘子图像目标区域分割,用最小外接矩形来框定橘子目标区域完成橘子区域识别,最后通过对最小外接矩形的计算,完成橘子的大小分级。试验结果表明:基于机器视觉的橘子大小分级平均准确率达96.7%,能够有效满足橘子的分级要求。Semi-mechanical method is often used for tangerine classification,which has low efficiency and uneven quality.This paper proposed a key technology for tangerine classification based on machine vision.The three channels of the original color image are converted into the gray image with only one channel by gray-scale,and the target area of the orange image is segmented by binarization,and the target area of the orange image is framed by the minimum external rectangle to realize the orange region recognition.Finally,the orange size is graded by calculating the smallest external rectangle.The experimental results show that the average accuracy of orange size classification based on machine vision is 96.7%,which can effectively meet the requirements of orange size classification.

关 键 词：橘子分级 机器视觉 特征提取 连通区域标记 

分 类 号：S375[农业科学—农产品加工]