基于高光谱成像技术和IRIV算法的玉米种子品种纯度识别

Recognition of maize seed variety purity based on hyperspectral imaging technology and IRIV algorithm

基于高光谱成像技术,提出了一种无损、快速的玉米种子纯度识别方法.首先,采用多元散射校正(MSC)等方法对数据进行预处理;其次,应用竞争性自适应重加权法(CARS)和迭代保留信息变量法(IRIV)提取特征波长;再次,建立支持向量机(SVM)和线性判别分析(LDA)等纯度识别模型;最后,设置随机种子值,使用采集函数“expected-improvement-plus”搜索置信区间中的待评价点,得到使交叉验证损失最小的超参数值,提高模型的准确率.结果表明:MSC-IRIV-LDA识别模型准确率最高,训练集和预测集的准确率分别为0.9604和0.9333,K值为0.9186;对LDA的δ和γ超参数值进行优化后,进一步提高了训练集、预测集准确率和K值;本研究提出的方法能够实现玉米种子纯度无损、快速识别,为精准农业的发展提供技术支持.

Based on the hyperspectral imaging technology,the non-destructive and rapid identification method for maize seed purity was proposed.The data were preprocessed by multiple scattering correction(MSC),and the competitive adaptive reweighted sampling(CARS)and iteratively retains informative variables(IRIV)were used to extract the characteristic wavelengths.The purity identification models of support vector machine(SVM)and line discriminant analysis(LDA)were established.The random seed value was set,and the points to be evaluated in the confidence interval were searched by the acquisition function of expected-improvement-plus to obtain the hyper parameter value with the minimum cross-validation loss for improving the model accuracy.The results show that the MSC-IRIV-LDA recognition model has the highest accuracy.The accuracies of the training set and the prediction set are respective 0.9604 and 0.9333,and the Kappa coefficient is 0.9186.After optimizing the Delta and Gamma hyper parameters of LDA,the accuracies of training set and prediction set and Kappa coefficient can be further improved.The proposed method can realize non-destructive and rapid identification of maize seed purity,which can provide technical support for the development of precision agriculture.