【摘要】 玫瑰鲜切花的分级检测对其销售有着重要意义，目前玫瑰鲜切花的分级检测主要采用人工的方式。为减少人工分级过程中造成玫瑰鲜切花的损失，基于机器视觉方法运用HALCON软件搭建了一套玫瑰鲜切花分级检测系统。首先，设计了试验平台，建立了玫瑰鲜切花分级标准。随后，加入了图像增强和数据增强技术，使图像效果得到改善，并增加了样本的数量，利用中值滤波法让图片噪声得到消除，保证了分类结果的准确性。最后，将训练样本加入5个模型中进行训练，比较每个模型的训练结果，选用Mobilenet＿v2模型加入图像分类系统对鲜切花俯视图分类，并建立一维测量系统测量花茎的长度；建立评判准则模型，完成对玫瑰鲜切花的分级。经测试，得到俯视图分类系统的分类准确率为94%,经一维测量的花茎长度都在误差允许范围内。更多还原

【Abstract】 The classification and detection of fresh cut rose is of great significance to its sales. At present, the classification and detection of fresh cut rose is mainly manual. In order to reduce the loss of fresh cut rose flowers in the process of manual classification, a set of classification and detection system of fresh cut rose flowers was built based on machine vision method and Halcon software. Firstly, the experimental platform was designed and the classification standard of fresh cut rose flowers was established. Then, image enhancement and data enhancement technology are added to improve the image effect, increase the number of samples, and use the median filter method to eliminate the image noise, so as to ensure the accuracy of classification results. Finally, the training samples are added to five models for training, and the training results of each model are compared. Mobilenet＿v2 model is selected to join the image classification system to classify the top view of fresh cut flowers, and a one-dimensional measurement system is established to measure the length of flower stems; Establish the evaluation criteria model to complete the classification of fresh cut rose flowers. After testing, the classification accuracy of the top view classification system is 94%, and the flower stem length measured by one dimension is within the error range.更多还原