基于机器视觉的金丝皇菊智能分级系统研究

为实现对金丝皇菊的快速无损等级评估，本文应用机器视觉技术对5个等级的金丝皇菊进行智能分级。首先根据金丝皇菊的品质特性设计分级装置，并根据金丝皇菊的颜色、形状、完整度等特征设置不同的分级标准；其次运用图像灰度化、图像去噪、图像增强技术完成金丝皇菊图像预处理；再次采用加色法混色模型(Red Green Blue, RGB)完成金丝皇菊颜色特征提取与识别，并通过图像分割和边缘检测技术完成对金丝皇菊图像完整度的判断及花径的计算，得出金丝皇菊的预测等级；最后基于Microsoft Visual Studio 2017平台开发一套金丝皇菊智能分级系统，实现系统的实时可视化操作。结果表明，本文设计的金丝皇菊智能分级系统整体分级准确率达到了97.6%，平均分级速度为人工的5倍多，其在可靠性、速度、工作效率、鲁棒性等各方面都优于传统的人工分级，本研究为机器视觉技术在花茶分级领域的应用提供了实际案例与技术参考。

Research on Intelligent Grading System of Imperial Chrysanthemum Based on Machine Vision

In order to realize the rapid nondestructive grade evaluation of Imperial Chrysanthemum,machine vision technology was applied to intelligently grade five grades of Imperial Chrysanthemum in this paper.Firstly,the grading device was designed according to the quality characteristics of Imperial Chrysanthemum,and different grading standards were set according to the color,shape,integrity and other characteristics of Imperial Chrysanthemum.Secondly,the image preprocessing of Imperial Chrysanthemum was completed by using image graying,image denoising and image enhancement technology.Thirdly,RGB model was used to complete the color feature extraction and recognition of Imperial Chrysanthemum,and the image integrity judgment and flower diameter calculation of Imperial Chrysanthemum were completed through image segmentation and edge detection technology,so as to obtain the prediction level of Imperial Chrysanthemum.Finally,a set of Imperial Chrysanthemum intelligent grading system was developed based on Microsoft Visual Studio 2017 platform to realize real-time visual operation.The results showed that the overall classification accuracy of the Imperial Chrysanthemum intelligent grading system designed in this paper reached 97.6%,and the average grading speed was more than 5 times that of manual classification.It was superior to the traditional manual classification in reliability,speed,work efficiency and robustness.This study provided a practical case and technical reference for the application of machine vision technology in the field of scented tea grading.