基于分裂Bregman算法的玉米种子品种识别

摘要

Corn seed purity is closely related to corn yield,so seed selection plays an important role in improving grain yield product.The automatic seed selection procedure based on the machine vision is usually divided into three steps:image segmentation,feature extraction and classification.Variational model for image segmentation and corresponding numerical technique of Split Bregman method were introduced into the identification procedure,which had advantages of feature extraction such as high accuracy and closed continuous border.In addition,the adaptive wavelet collocation method was employed to solve the optimality conditions in Bregman split method.Based on the improved method,the corn geometric features can be extracted more precisely.Nongda108 and Ludan981 were taken as examples to test the new method.Based on a classifier designed with SVM,results showed the identification accuracy of Nongda108 and Ludan981 were 97.3％ and 98％,respectively,better than 95％ in previous research.%玉米品种的纯度和玉米产量密切相关,因此玉米品种的筛选对提高粮食产量具有非常重要的作用.基于机器视觉的自动品种筛选技术通常分为图像分割、特征获取和分类等三步.图像分割的精度直接决定了种子识别准确度.在众多的图像分割技术中,本研究尝试将图像分割变分模型及其对应的数值求解方法-分裂Bregman算法应用于玉米种子自动识别中.该方法具有精度高,分割边界封闭连续等有利于玉米特征提取的优点.此外,本文还将自适应小波配置法用于求解分裂Bregman算法中的最优条件,得到一种更为精确高效的分裂Bregman算法.进而结合改进分裂Bregman算法得到的不同玉米品种特征和支持向量机技术得到了一种新的玉米品种分类器.采用该方法对玉米品种农大108和鲁丹981进行实验,识别精度分别达到97.3％和98％,相对于由其他分割方法得到的分类结果精度(95％)要高.