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OACSTPCD

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.