Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology
Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology
复制标题
基于高光谱成像技术的大豆种子品种鉴定
DOI：
10.3390/s19235225
复制
发表时间：
2019-12-01
期刊：
影响因子：
3.9
通讯作者：
Huang, Zhongwen
中科院分区：
文献类型：
--
作者：
Zhu, Shaolong;Chao, Maoni;Huang, Zhongwen
高光谱成像是一种集光谱学和图像学技术于一体的无损检测技术，它使我们能够快速获取物体的内部和外部信息，并对作物种子品种进行识别。首先，采集了10个大豆种子品种的高光谱图像，得到了其反射率。采用Savitzky-Golay平滑（SG）、一阶导数（FD）、标准正态变量（SNV）、快速傅立叶变换（FFT）、希尔伯特变换（HT）和乘法散射校正（MSC）光谱反射预处理方法。然后，采用竞争自适应重加权采样（CARS）、逐次投影算法（SPA）和主成分分析（PCA）提取预处理后的光谱反射率数据的特征波长和特征信息；最后，采用贝叶斯、支持向量机（SVM）、k近邻（KNN）、集成学习（EL）和人工神经网络（ANN） 5种分类器对种子品种进行识别。结果表明，在90个组合中，MSC-CARS-EL的交叉验证准确率最高，训练集、测试集和5倍交叉验证准确率分别为100%、100%和99.8%。光谱预处理对识别精度的贡献高于特征提取和分类器选择。预处理方法决定了识别精度的范围，特征选择方法和分类器只在该范围内变化。实验结果为其他作物种子品种的鉴定提供了很好的参考。
Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.