|
|
|
| Design and experiment of non-destructive testing and grading system for citrus quality based on machine vision and spectral fusion |
| WEN Tao, DAI Xingyong, LI Lang, LIU Hao |
| College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha， Hunan 410004, China |
|
|
|
| Guide |
|
|
Abstract For the three key quality indicators of citrus fruit diameter, coloring rate and internal sugar level, the citrus comprehensive quality non-destructive testing and grading system was designed based on the double-cone roller fruit cup transmission line with feeding part, machine vision detection module, near-infrared spectroscopy detection module and grading execution part. In the machine vision detection module， the single camera was used to capture videos of rolling citrus for obtaining large number of citrus images in different postures and performing contour extraction. The fruit diameter was calculated with the average value of the minimum external circle diameter of all single citrus frame images, and the average value of its two-dimensional yellow proportion obtained by each frame image was used as the full surface coloring rate. In the near-infrared spectrum detection module, the transmission light path was designed to collect the citrus transmittance spectrum, and the mixed attitude sugar detection model was established according to the two high-frequency attitudes of citrus during on-line detection. Comparing the modeling results under different pretreatment methods, the partial least squares (PLS) model established after applying the more effective multi-scattering correction (MSC) was selected. The on-line testing results show that the maximum absolute error of fruit diameter detection is -1.42 mm with the maximum absolute error of coloring rate detection of 0.048, and the correlation coefficient of the sugar test results is 0.817 with the root mean square error of 0.658%. The joint detection and grading method of internal and external quality determines the joint grading methods of three qualities according to the decision-making method of the discriminant tree. At the sorting speed of 5/s, the average accuracy of the comprehensive grading can reach 91.16%. The overall structure of the detection and grading system is simple with strong applicability for sphere-like fruits, which has great potential for industrial application.
|
|
Received: 12 April 2022
|
|
|
| Fund: |
|
|
|
| ［1］ |
卢军,李婷,黄琪悦,等.柑橘品质分级要素的分析与评价[J].湖北农业科学,2012,51(20): 4631-4633.
|
|
LU J, LI T, HUANG Q Y, et al. Analysis and evaluation on the grading factors of citrus[J]. Hubei Agricultural Sciences, 2012,51(20): 4631-4633.(in Chinese)
|
| ［2］ |
孙海霞,张淑娟,薛建新,等.基于机器视觉和光谱技术水果分级的研究进展[J].农机化研究,2014,36(1): 234-237.
|
|
SUN H X, ZHANG S J, XUE J X, et al. Research progress of fruit grading technology based on machine vision and spectrum technology[J]. Journal of Agricultural Mechanization Research,2014,36(1): 234-237.(in Chinese)
|
| ［3］ |
张欣欣,李跑,余梅,等.柑橘近红外光谱无损检测技术研究进展[J].食品科学,2022,43(1): 260-268.
|
|
ZHANG X X, LI P, YU M, et al. Progress in non-destructive citrus quality detection using near-infrared spectroscopy[J]. Food Science,2022,43(1): 260-268.(in Chinese)
|
| ［4］ |
向阳,林洁雯,李亚军,等.芒果双面成熟度在线检测分级系统[J].农业工程学报,2019,35(10): 259-266.
|
|
XIANG Y, LIN J W, LI Y J, et al. Mango double-sided maturity online detection and classification system[J]. Transactions of the Chinese Society of Agricultural Engineering , 2019, 35(10): 259-266.(in Chinese)
|
| ［5］ |
赵娟,彭彦昆,Sagar Dhakal,等.基于机器视觉的苹果外观缺陷在线检测[J].农业机械学报,2013,44(增刊1): 260-263.
|
|
ZHAO J, PENG Y K, SAGAR D, et al. On-line detection of apple surface defect based on image processing method[J]. Transactions of the Chinese Society of Agricultural Machinery, 2013, 44(Sup1): 260-263.(in Chinese)
|
| ［6］ |
郭志明,黄文倩,陈全胜,等.近红外光谱的苹果内部品质在线检测模型优化[J].现代食品科技,2016,32(9): 147-153.
|
|
GUO Z M, HUANG W Q, CHEN Q S, et al. Model optimization for the on-line inspection of internal apple quality by shortwave near-infrared spectroscopy[J]. Modern Food Science & Technology, 2016,32(9): 147-153.(in Chinese)
|
| ［7］ |
刘燕德,翟建龙.脐橙可溶性固形物的在线近红外光谱检测[J].西北农林科技大学学报(自然科学版),2014,42(3): 186-190.
|
|
LIU Y D, ZHAI J L. Online detection of soluble solids in navel orange using near-infrared diffuse spectroscopy[J]. Journal of Northwest A & F University(Natural Science Edition), 2014, 42(3): 186-190.(in Chinese)
|
| ［8］ |
李江波,黄文倩,张保华,等.类球形水果表皮颜色变化校正方法研究[J].农业机械学报,2014,45(4): 226-230.
|
|
LI J B, HUANG W Q, ZHANG B H, et al. Correction algorithm of lighting non-uniformity on spherical fruit[J]. Transactions of the Chinese Society of Agricultural Machinery,2014,45(4): 226-230.(in Chinese)
|
| ［9］ |
陆辉山.水果内部品质可见/近红外光谱实时无损检测关键技术研究[D].杭州:浙江大学,2006.
|
| ［10］ |
郭志明,黄文倩,陈全胜,等.苹果腐心病的透射光谱在线检测系统设计及试验[J].农业工程学报,2016,32(6): 283-288.
|
|
GUO Z M, HUANG W Q, CHEN Q S, et al. Design and test of on-line detection system for apple core rot disease based on transmitted spectrum[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(6): 283-288.(in Chinese)
|
| ［11］ |
潘圆媛,刘燕德,孙旭东.水果内部品质近红外动态在线检测研究进展[J].激光与红外,2010,40(8): 805-810.
|
|
PAN Y Y, LIU Y D, SUN X D. Research progress of on-line detection of fruit quality using near infrared spectroscopy[J]. Laser and Infrared, 2010, 40(8): 805-810.(in Chinese)
|
| ［12］ |
孙荣荣,宋健宇,张明,等.中国柑橘外部品质机器视觉检测分级技术研究现状与展望[J].农业工程,2019,9(1): 47-51.
|
|
SUN R R, SONG J Y, ZHANG M, et al. Application of machine vision in external quality detection and grading of citrus in China[J]. Agricultural Engineering,2019,9(1): 47-51.(in Chinese)
|
| ［13］ |
徐晓.苹果检测姿态与双点检测对苹果可溶性固形物含量可见/近红外光谱在线检测的影响研究[D].杭州:浙江大学,2019.
|
|
|
|
