Abstract Nondestructive identification of wheat grains in different states plays an important role in improving the quality of wheat products. This study investigated the possibility of using hyperspectral imaging techniques to discriminate healthy wheat grain, germinated wheat grain, mildewed wheat grain, and shriveled wheat grain (wheat grain infected with fusarium head blight). Both sides of individual wheat kernels were subjected to hyperspectral imaging (866.4–1701.0 nm) to acquire hyperspectral cube data. Spectral data were preprocessed by using standardization and multiple scattering correction. In addition, the principal component loading method was used to extract the characteristic wavelengths of both sides of wheat grains. The sample is divided into calibration set, test set, and validation set. The data of the calibration set are used to train the partial least squares discriminant analysis model, K-nearest neighbor model, and the support vector machine model, and the test set data are used to test the model. The results show that spectral data of both sides can achieve good classification results, while the reverse spectral data perform better. By comparing with each other, the support vector machine model is selected as the best classification model. Finally, using two hyperspectral images (reverse side) that are not involved in training and testing to verify the accuracy of the established support vector machine model, and the classification effect maps of the four wheat grains were visualized. The results indicate that nondestructive classification of wheat grains in different states is feasible based on hyperspectral imaging technology.

中文翻译：

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基于高光谱成像技术的不同状态小麦籽粒识别

摘要 小麦籽粒不同状态的无损鉴定对提高小麦产品质量具有重要作用。本研究调查了使用高光谱成像技术区分健康小麦籽粒、发芽小麦籽粒、发霉小麦籽粒和枯萎小麦籽粒（受镰刀菌枯萎病感染的小麦籽粒）的可能性。对单个小麦籽粒的两侧进行高光谱成像（866.4-1701.0 nm）以获得高光谱立方体数据。通过使用标准化和多重散射校正对光谱数据进行预处理。此外，采用主成分加载法提取小麦籽粒两侧的特征波长。样本分为校准集、测试集和验证集。校准集数据用于训练偏最小二乘判别分析模型、K-近邻模型和支持向量机模型，测试集数据用于测试模型。结果表明，双方的光谱数据都能达到较好的分类效果，而反向光谱数据表现更好。通过相互比较，选择支持向量机模型作为最佳分类模型。最后，利用两张未参与训练和测试的高光谱图像（反面）验证所建立的支持向量机模型的准确性，并对四种小麦粒的分类效果图进行可视化。