Abstract A novel method for discriminating the varieties of sweet maize seeds was developed on the basis of hyperspectral imaging technology in the visible and near-infrared (Vis–NIR) region (326.7–1098.1 nm). First, the Vis–NIR hyperspectral images of nine varieties of sweet maize seeds were obtained with the orientations of germ up and down. Second, Savitzky–Golay (SG) smoothing and first derivative (FD) methods were used to highlight the differences of different maize seeds. Finally, a variety discrimination model was established by support vector machine (SVM) based on the effective wavelengths extracted by competitive adaptive reweighted sampling (CARS) algorithm. Additionally, the performance of other six comparative algorithms including successive projections algorithm (SPA), principal component analysis (PCA), factor analysis (FA), random projection (RP), independent component analysis (ICA), and t-distributed stochastic neighbor embedding (t-SNE) were compared with CARS. The classification models of SVM was also compared with Naive Bayes (NB), K-nearest neighbors (KNN), artificial neural networks (ANN), decision tree (DT), linear discriminant analysis (LDA) and logistic regression (LR) algorithms. Results showed that the SG + FD + CARS + SVM model achieved the best performance for discrimination of nine varieties of sweet maize seeds with classification accuracies of 94.07% and 94.86% for germ up and germ down orientations respectively, which is promising to be a new approach for discrimination the variety of sweet maize seeds.

中文翻译：

---

基于高光谱图像结合波长选择算法的甜玉米品种无损判别

摘要 基于可见光和近红外（Vis-NIR）区域（326.7-1098.1 nm）的高光谱成像技术，开发了一种鉴别甜玉米种子品种的新方法。首先，获得了九个品种的甜玉米种子的 Vis-NIR 高光谱图像，其中胚芽方向为上下。其次，使用 Savitzky-Golay (SG) 平滑和一阶导数 (FD) 方法来突出不同玉米种子的差异。最后，基于竞争自适应重加权采样（CARS）算法提取的有效波长，通过支持向量机（SVM）建立品种判别模型。此外，其他六种比较算法的性能，包括连续投影算法（SPA）、主成分分析（PCA）、因子分析（FA）、随机投影 (RP)、独立分量分析 (ICA) 和 t 分布随机邻域嵌入 (t-SNE) 与 CARS 进行了比较。SVM的分类模型还与朴素贝叶斯（NB）、K-最近邻（KNN）、人工神经网络（ANN）、决策树（DT）、线性判别分析（LDA）和逻辑回归（LR）算法进行了比较。结果表明，SG + FD + CARS + SVM 模型对 9 种甜玉米种子的判别性能最佳，胚芽向上和胚芽向下方向的分类准确率分别为 94.07% 和 94.86%，有望成为一种新的分类方法。区分甜玉米种子品种的方法。SVM的分类模型还与朴素贝叶斯（NB）、K-最近邻（KNN）、人工神经网络（ANN）、决策树（DT）、线性判别分析（LDA）和逻辑回归（LR）算法进行了比较。结果表明，SG + FD + CARS + SVM 模型对 9 种甜玉米种子的判别性能最佳，胚芽向上和胚芽向下方向的分类准确率分别为 94.07% 和 94.86%，有望成为一种新的分类方法。区分甜玉米种子品种的方法。SVM的分类模型还与朴素贝叶斯（NB）、K-最近邻（KNN）、人工神经网络（ANN）、决策树（DT）、线性判别分析（LDA）和逻辑回归（LR）算法进行了比较。结果表明，SG + FD + CARS + SVM 模型对 9 种甜玉米种子的判别性能最佳，胚芽向上和胚芽向下方向的分类准确率分别为 94.07% 和 94.86%，有望成为一种新的分类方法。区分甜玉米种子品种的方法。