柑橘全表面色泽在线检测与分级系统
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李浪,男,中南林业科技大学在读硕士研究生

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湖南省自然科学基金(编号:2020JJ4142);湖南省林业杰青培养科研项目(编号:XLK202108-7);湖南省教育厅科学研究重点项目(编号:20A515)


Online detection and grading system for citrus full-surface color
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    摘要:

    目的:实现柑橘色泽快速、准确分选。方法:建立了一种基于机器视觉的柑橘在线色泽检测与分级系统,系统由上料单元、链式输送机构、图像采集系统和分果单元组成。利用工业相机配合翻滚机构均匀拍摄50~60帧运动中的柑橘图像,获取柑橘完整表面信息。无损检测软件对实时获取的每一帧图像进行预处理,得到二维着色比值,对该比值进行动态跟踪存储,取二维着色比值的算术平均值以降低重复区域对柑橘表面着色率计算的影响,最后对计算的着色率进行判别分级。结果:在6个/s的分级速度下,系统计算的柑橘色泽占比最大误差为5%,分级准确率为90.54%。结论:该系统能够满足柑橘色泽快速、准确的分选需求。

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    Objective: This study aimed to realize the fast and accurate sorting of citrus color. Methods: An online color detection and grading system of citrus based on machine vision was designed in this study. The system is composed of feeding unit, chain conveying mechanism, image acquisition system and fruit dividing unit. The industrial camera which was combined with the rolling mechanism was used to uniformly capture 50~60 frames of images for obtaining the complete surface information of citrus. The non-destructive testing software preprocessed each frame of images acquired in real time to obtain the two-dimensional coloring ratio, which is dynamically tracked and stored. The arithmetic mean value of two-dimensional coloring ratio was taken to reduce the influence of the repeated areas on the coloring rate calculation of citrus surface, and finally the calculated coloring rate was discriminated and graded. Results: The experimental results showed that when the transmission speed was 6s-1,the maximum error of citrus coloring proportion calculated by the system was 5%, and the sorting accuracy rate was 90.54%. Conclusion: The system can meet the needs of fast and accurate sorting of citrus color.

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李浪,文韬,代兴勇,等.柑橘全表面色泽在线检测与分级系统[J].食品与机械,2022,(12):121-126.
LI Lang, WEN Tao, DAI Xing-yong, et al. Online detection and grading system for citrus full-surface color[J]. Food & Machinery,2022,(12):121-126.

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  • 在线发布日期: 2023-02-28
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