文献类型： 外文期刊

第一作者： Song Shao-zhong

作者： Song Shao-zhong;Liu Jun-ling;Liu Yuan-yuan;Gao Xun;Zhou Zi-yang;Teng Xing;Li Ji-hong

作者机构：

关键词： Sorghum; Hyperspectral imaging; Machine learning algorithm; Breed identification

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.7； 五年影响因子：0.6 ）

ISSN： 1000-0593

年卷期： 2024 年 44 卷 5 期

页码：

收录情况： SCI

摘要： Sorghum is an important raw material for liquor brewing. The components of sorghum are very important to the trace components and quality of liquor, and the quality of sorghum can affect the quality and flavor of liquor. Therefore, the nondestructive and rapid identification of sorghum breeds is an urgent and important question for improving the quality of liquor. In this paper, hyperspectral imaging technology combined with a machine learning algorithm is used to classify and identify sorghum breeds. By using the hyperspectral imaging technology, hyperspectral spectral lines and image texture data of 10 breeds of sorghum are obtained at the same time. Multivariate scattering correction (MSC) is used to preprocess the hyperspectral spectrum, and a continuous projection algorithm (SPA) is used to screen 62 feature bands. The gray level co-occurrence matrix extracts four texture features of sorghum. The hyperspectral spectral data and spectral-image fusion data are used, respectively, and four machine learning algorithms, including PLS-DA, SVM, ELM and RF, are used to classify and identify the sorghum breed. The results show that the hyperspectral characteristic bands extracted by SPA dimensionality reduction can be represented by the data information of the full hyperspectral spectral information after MSC pretreatment, which improves the stability of the PLS-DA algorithm model in the identification of the sorghum breed. The identification accuracy of 10 breeds of sorghum is improved from 67.58% to 93.85% and the identification accuracy is increased by 27%. After the fusion of hyperspectral spectral data and image texture feature data, the identification accuracy of the sorghum breed by using the PLS-DA model under the conditions of full-spectrum and feature spectrum is improved to 96.47% and 97.16%, respectively, which is more suitable for the classification and identification of sorghum breed compared with the single hyperspectral data. Compared with the results of SVM, ELM, and RF machine learning algorithms, the PLS-DA machine learning algorithm model has the best identification accuracy for the sorghum breed. The research has proved the effectiveness of hyperspectral imaging technology combined with machine learning algorithms in the identification of sorghum breeds, which can achieve fast and accurate quality inspection of sorghum products.

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