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.
文韬， 代兴勇， 李浪， 刘豪. 基于机器视觉与光谱融合的柑橘品质无损检测分级系统设计与试验[J]. 江苏大学学报（自然科学版）, 2024, 45(1): 38-45.
WEN Tao, DAI Xingyong, LI Lang, LIU Hao. Design and experiment of non-destructive testing and grading system for citrus quality based on machine vision and spectral fusion[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2024, 45(1): 38-45.
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
