摘要
为了满足广大果农对采后脐橙及时分级的需求,设计一套基于机器视觉的脐橙采后田间分级系统,由输送系统、视觉系统和分拣系统组成.该系统能检测脐橙的大小、表面缺陷数量及缺陷面积,并根据分级标准对水果进行自动分级.试验结果表明,单幅图片的平均检测时间小于30 ms,尺寸的检测误差小于3%,缺陷检测率为99%,缺陷面积检测误差小于7%.与传统检测系统相比,该系统检测速度快,结构紧凑,适合脐橙的田间分级检验.
To meet the demand of fruit farmers' timely grading of navel oranges,a set of navel orange postharvest field grading system was designed based on machine vision,which was composed of conveying system,visual system and sorting system. The size of navel orange,the number of surface defects and the defect area were detected by the proposed system. The fruit was automatically graded according to the predetermined comprehensive assessment standard. The experimental results show that the average detection time of single picture is less than 30 ms,and the detection error of size is less than 3% with defect detection rate of 99% and defect area detection error less than 7%. Compared with the traditional detection system,the proposed system has the advantages of high detection speed and compact design,which is suitable for in-filed grading of navel orange.
引文
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