激光技术2024，Vol.48Issue(2)：281-287,7.DOI:10.7510/jgjs.issn.1001-3806.2024.02.021
基于SMOTE-UVE-SVM的小麦种子纯度高光谱图像检测
Hyperspectral image detection of wheat seed purity based on SMOTE-UVE-SVM
摘要
Abstract
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.关键词
光谱学/高光谱成像技术/合成少数类过采样技术/非信息变量剔除/种子纯度Key words
spectroscopy/hyperspectral imaging technology/synthetic minority oversampling technique/uninformative variables elimination/seed purity分类
计算机与自动化引用本文复制引用
朱潘雨,黄敏,赵鑫..基于SMOTE-UVE-SVM的小麦种子纯度高光谱图像检测[J].激光技术,2024,48(2):281-287,7.基金项目
国家自然科学基金青年基金资助项目(62205128) （62205128）
