基于机器视觉的小粒咖啡豆的检测技术
|
摘要
设计搭建了基于机器视觉的小粒咖啡豆检测分级系统,系统由进料部、匀料筛分部、色选部、气动部、收料部以及电控箱组成。开发了基于OpenCV和visual studio的系统分析与控制软件,实现咖啡豆果径宽度和烘焙程度的检测分级。基于Blob分析方法对小粒咖啡生豆进行图像分割,利用最小外接矩形方法对果径宽度进行特征提取,采用HSV颜色空间模型提取小粒咖啡豆的颜色特征值,最终将小粒咖啡生豆分为5个等级,将烘焙程度分为浅度、中度、深度3个程度。系统运行验证试验结果表明,对小粒咖啡生豆的果径宽度检测的平均误差为1.275%,烘焙程度检测的准确率为88.9%。
The detection and grading system of small grain coffee bean was designed based on the machine vision. The system consisted of the feeding part, the sizing part, the color selection part, the pneumatic part, the receiving part and the electric control box. The system analysis and control software based on OpenCV and visual studio was developed to realize the detection and classification of coffee bean diameter and baking degree. Based on Blob analysis method, the image was extracted by using the minimum circumscribed rectangle method. The color feature value of small coffee beans was extracted by HSV color space model. The degree of baking is identified. Finally, the small–grain coffee green beans are divided into five grades and the degree of baking is divided into three levels: shallow, medium, and deep. The verification test results of the system showed that the average error of the detection of the diameter of the small bean coffee beans was 1.275% and the accuracy of the baking degree detection was 88.9%.
The detection and grading system of small grain coffee bean was designed based on the machine vision. The system consisted of the feeding part, the sizing part, the color selection part, the pneumatic part, the receiving part and the electric control box. The system analysis and control software based on OpenCV and visual studio was developed to realize the detection and classification of coffee bean diameter and baking degree. Based on Blob analysis method, the image was extracted by using the minimum circumscribed rectangle method. The color feature value of small coffee beans was extracted by HSV color space model. The degree of baking is identified. Finally, the small–grain coffee green beans are divided into five grades and the degree of baking is divided into three levels: shallow, medium, and deep. The verification test results of the system showed that the average error of the detection of the diameter of the small bean coffee beans was 1.275% and the accuracy of the baking degree detection was 88.9%.
引文
[1]周艳飞,陈治华.运转产业链加快云南咖啡产业发展[J].中国热带农业,2010(5):27–30.
[2]郭容琦,罗心平,李国鹏,等.云南小粒咖啡产业发展现状分析[J].广东农业科学,2009,36(3):209–212.
[3]王莉.云南农产品品牌营销研究——以云南小粒咖啡为例[J].中国商论,2016(19):7–8.
[4]叶广.小粒咖啡发展现状与未来前景[J].农家科技,2015(9):141.
[5] THOMAS P,KANNAN A,DEGWEKAR V H,et al.Non–destructive detection of seed weevil–infested mango fruits by X–ray imaging[J]. Postharvest Biology and Technology,1995,5(1):161–165.
[6] HEINEMANN P H,PATHARE N P,MORROW C T. An automated inspection station for machine–vision grading of potatoes[J]. Machine Vision and Applications,1996,9(1):14–19.
[7] AFRISAL H,FARIS M,GUNTUR U P,et al. Portable smart sorting and grading machine for fruits using computer vision[C]//International Conference on Computer.Jakarta:IEEE,2013:71–75.
[8]王风云,封文杰,郑纪业,等.基于机器视觉的双孢蘑菇在线自动分级系统设计与试验[J].农业工程学报,2018,34(7):256–263.
[9]海潮,赵凤霞,孙烁.基于Blob分析的红枣表面缺陷在线检测技术[J].食品与机械,2018,34(1):126–129.
[10]展慧,李小昱,周竹,等.基于近红外光谱和机器视觉融合技术的板栗缺陷检测[J].农业工程学报,2011,27(2):345–349.
[11]杨涛,张云伟,苟爽.基于机器视觉的草莓自动分级方法研究[J].食品与机械,2018,34(3):146–150.
[12]周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012,28(7):178–183.
[13] DB/T371—2012地理标志产品保山小粒咖啡[S].
[14]余烨,李冰飞,张小魏,等.面向RGBD图像的标记分水岭分割[J].中国图象图形学报,2016,21(2):145–154.
[15]任玉刚,张建,李淼,等.基于分水岭算法的作物病害叶片图像分割方法[J].计算机应用,2012,32(3):752–755.
[16]张新良,马明全,赵运基.基于图像形态学运算和Hough变换的油箱盖检测[J].制造业自动化,2018,40(7):119–122.
[17] GONEALEZ R C,WOODE R E.数字图像处理[M].阮秋琦,译.北京:电子工业出版社,2011.
[18]张凯兵,章爱群,李春生.基于HSV空间颜色直方图的油菜叶片缺素诊断[J].农业工程学报,2016,32(19):179–187.
[19]吕文佳,刘云,杨剀舟,等.咖啡主要烘焙风味物质的形成及变化规律[J].食品工业科技,2015,36(3):394–400.
[20]周斌,任洪涛.烘焙程度对云南小粒咖啡香气品质的影响[J].食品研究与开发,2014,35(22):68–73.
[2]郭容琦,罗心平,李国鹏,等.云南小粒咖啡产业发展现状分析[J].广东农业科学,2009,36(3):209–212.
[3]王莉.云南农产品品牌营销研究——以云南小粒咖啡为例[J].中国商论,2016(19):7–8.
[4]叶广.小粒咖啡发展现状与未来前景[J].农家科技,2015(9):141.
[5] THOMAS P,KANNAN A,DEGWEKAR V H,et al.Non–destructive detection of seed weevil–infested mango fruits by X–ray imaging[J]. Postharvest Biology and Technology,1995,5(1):161–165.
[6] HEINEMANN P H,PATHARE N P,MORROW C T. An automated inspection station for machine–vision grading of potatoes[J]. Machine Vision and Applications,1996,9(1):14–19.
[7] AFRISAL H,FARIS M,GUNTUR U P,et al. Portable smart sorting and grading machine for fruits using computer vision[C]//International Conference on Computer.Jakarta:IEEE,2013:71–75.
[8]王风云,封文杰,郑纪业,等.基于机器视觉的双孢蘑菇在线自动分级系统设计与试验[J].农业工程学报,2018,34(7):256–263.
[9]海潮,赵凤霞,孙烁.基于Blob分析的红枣表面缺陷在线检测技术[J].食品与机械,2018,34(1):126–129.
[10]展慧,李小昱,周竹,等.基于近红外光谱和机器视觉融合技术的板栗缺陷检测[J].农业工程学报,2011,27(2):345–349.
[11]杨涛,张云伟,苟爽.基于机器视觉的草莓自动分级方法研究[J].食品与机械,2018,34(3):146–150.
[12]周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012,28(7):178–183.
[13] DB/T371—2012地理标志产品保山小粒咖啡[S].
[14]余烨,李冰飞,张小魏,等.面向RGBD图像的标记分水岭分割[J].中国图象图形学报,2016,21(2):145–154.
[15]任玉刚,张建,李淼,等.基于分水岭算法的作物病害叶片图像分割方法[J].计算机应用,2012,32(3):752–755.
[16]张新良,马明全,赵运基.基于图像形态学运算和Hough变换的油箱盖检测[J].制造业自动化,2018,40(7):119–122.
[17] GONEALEZ R C,WOODE R E.数字图像处理[M].阮秋琦,译.北京:电子工业出版社,2011.
[18]张凯兵,章爱群,李春生.基于HSV空间颜色直方图的油菜叶片缺素诊断[J].农业工程学报,2016,32(19):179–187.
[19]吕文佳,刘云,杨剀舟,等.咖啡主要烘焙风味物质的形成及变化规律[J].食品工业科技,2015,36(3):394–400.
[20]周斌,任洪涛.烘焙程度对云南小粒咖啡香气品质的影响[J].食品研究与开发,2014,35(22):68–73.