基于SMOTE-UVE-SVM的小麦种子纯度高光谱图像检测. (Chinese)
In: Laser Technology, Jg. 48 (2024-03-01), Heft 2, S. 281-287
academicJournal
Zugriff:
In order to solve the problem, the performance of the wheat seed purity detection model decreased due to sample imbalance and band information redundancy in the process of hyperspectral imaging. A seed purity hyperspectral detection model was proposed by combining the synthetic minority oversampling technique (SMOTE) with uninformative variables elimination (UVE) and support vector machine (SVM). In this model, the SMOTE was used to expand the minority class (impurity) samples of the wheat seeds to improve the sample imbalance. At the same time, the UVE was used to select the high-dimensional hyperspectral features, and the SVM model was constructed to further reduce the risk of model overfitting caused by feature redundancy. Results showed that: The average accuracy, precision, and negative sample detection rate of the five types of wheat seeds are 95. 98%, 94. 94%, and 89. 32%, respectively, which are 3. 89%, 7. 18%, and 12. 42% higher than the traditional methods, respectively. The proposed method has a good application prospect in the detection of wheat seed purity based on hyperspectral imaging technology. [ABSTRACT FROM AUTHOR]
为了解决基于高光谱成像技术的小麦种子纯度检测过程中样本不均衡及波段信息冗余导致纯度检测模型性 能下降的问题,提出了一种融合合成少数类过采样技术(SMOTE)、非信息变量剔除(UVE)和支持向量机(SVM)的种子 纯度高光谱检测模型。该模型利用SMOTE 算法对小麦种子少数类(杂质)样本进行扩充,改善样本的不均衡性;同时利 用UVE 对高维的高光谱特征进行选择,并构建SVM 模型作为分类器,以进一步提高分类的性能。结果表明,5 类小麦种 子的平均准确率、精确率和负样本检出率分别达到95. 98%、94. 94%和89. 32%,较传统方法分别提高了3. 89%、7. 18% 和12. 42%。所提出的方法在基于高光谱成像技术的小麦种子纯度检测中具有较好的应用前景. [ABSTRACT FROM AUTHOR]
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Titel: |
基于SMOTE-UVE-SVM的小麦种子纯度高光谱图像检测. (Chinese)
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Autor/in / Beteiligte Person: | 朱潘雨 ; 黄敏 ; 赵鑫 |
Zeitschrift: | Laser Technology, Jg. 48 (2024-03-01), Heft 2, S. 281-287 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 1001-3806 (print) |
DOI: | 10.7510/jgjs.issn.1001-3806.2024.02.021 |
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