In view of the easy dehydration, yellowing and rotting of leaf vegetables, harmful substances produced in the human body, and being time-consuming and difficult to select leaf vegetables artificially, this study focused on development of nondestructive online detection system for leaf vegetables quality detection in small and medium restaurants based on machine vision. This system was composed of hardware modules and software processing system. The hardware modules included leaf vegetables separation unit, transmission unit, LED (light emitting diode) light sources unit, sensing unit, machine vision detection unit and sorting unit. The leaf vegetables separation could divide samples into pieces by designing a roller with a negative pressure. The sensing unit could control imaging acquisition and sorting operation of defective samples. In addition, the circuit of sorting unit included 555 timer, solenoid valve, relays and air compressor. The software was designed by using OpenCv and Visual C++ for realizing the automatic collection, data analysis and result display of the sample information. Finally, combining the characteristics of the corresponding information extraction method, the 320 spinach samples were adopted for verifying the system's performance, and among these samples, the yellow leaf, leaves with insect hole, rotting leaves and normal leaves were 91, 75, 91, and 63 respectively. For yellow leaves and rotting leaves, the RGB (red, green and blue) and HSV (hue, saturation, value) color space transform method were used by setting weight value H∈(60°, 130°), S∈(0, 0.17) and V∈(0, 0.23) to extract the characteristic information of the yellow leaves and rotting leaves of spinach. For leaves with insect hole, the threshold of 2G-R-B in RGB color space was adjusted for completing image graying, binarization and morphological denoising to extract contour feature information of leaves with insect hole, which could realize the discrimination of this kind of leaves and their area calculation. By the color space transformation of RGB to HSV, the threshold segmentation of hue variable with effective suppression of noise was selected to extract the characteristic region of yellowing leaves and rotten leaves. In order to obtain the correct information of insect eye contour, the closed operation of morphological filtering was used to remove the blade handle information. The experimental results show that the sorting system and information extraction algorithm can achieve the discrimination of spinach external quality. Compared with the artificial discrimination, the discrimination accuracies of yellow leaf, rotting leaves free of pests and leaves with insect hole were 96.70%, 92.59% and 84.62% respectively. The overall discrimination accuracy rate was 94.69%, and the selection speed of the device could reach 1 sample/0.84 s. The results also demonstrate that the sorting speed and the sorting accuracy of this system can meet the requirements of the quality separation of leafy vegetables. The test results show that the parameters of the system can meet the requirements of the quality separation. Compared with the quality of the artificially sorting vegetables, the system not only is easy to operate and time-saving, but also has good reliability. This research provides technical support for the practical research and development of leaf quality sorting device.%该研究从中式菜肴原料的品质控制需求出发,针对叶菜变黄、腐烂、虫害危害消费者健康、人工挑选费时费力的技术难点,开发了基于机器视觉的叶菜外部品质在线检测与分级系统,以实现在中小型餐厅和家庭场合对黄化叶、腐烂叶以及虫眼叶的快速检测判别与分级.该系统硬件部分主要包括基于负压吸气式的样品分离单元、基于LED光源照明系统的样品检测单元以及基于气吹式分选单元等.利用OpenCv和Visual C++编写了叶菜外部品质无损快速检测软件,实现样品信息的自动采集、数据分析和结果显示.最后结合对应的特征信息提取方法,利用320个菠菜样品对装置性能进行试验验证,其中黄化叶、虫眼叶、腐烂叶以及正常叶数目各为91、75、91、63.针对黄化叶与腐烂叶,利用 RGB 到HSV的颜色空间变换方法分别设定分量值H∈(60°, 130°)、S∈(0, 0.17)和V∈(0, 0.23)提取菠菜黄化叶与腐烂叶的特征信息,实现菠菜黄叶与腐烂叶的判别;针对虫眼叶,在RGB颜色空间中调节2G-R-B的阈值对虫眼叶菜进行灰度化、二值化及形态学消噪提取虫眼叶轮廓特征信息,实现虫眼叶判别及虫眼面积的计算.试验结果表明,利用该装置并结合各自的特征信息提取算法,能够实现对菠菜外部品质的判别,与人工判别相比,黄化叶、腐烂叶以及虫眼叶的判别正确率分别为96.70%、92.59%、84.62%,整体判别正确率为94.69%,1个叶菜样品的分选时间为0.84 s.
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