| 张 航，姚传安，蒋梦梦，姬豫航，李华杰.基于高光谱图像技术的小麦种子分类识别研究[J].麦类作物学报,2019,(1):96 |
| 基于高光谱图像技术的小麦种子分类识别研究 |
| Research on Wheat Seed Classification and Recognition Based on Hyperspectral Imaging |
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| DOI：10.7606/j.issn.1009-1041.2019.01.13 |
| 中文关键词: 高光谱图像 小麦种子 多籽粒分类 主成分分析 支持向量机 |
| 英文关键词:Hyperspectral imaging Wheat seed Multi-grain classification Principal component analysis Support vector machine |
| 基金项目:2017年河南省科技攻关计划项目（172102110161）；河南农业大学本科实验室开放创新训练团队项目（KF1505） |
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| 中文摘要: |
| 为了探讨高光谱图像技术在小麦种子分类识别中应用的可行性，采集了河南地区主要种植的7个小麦品种的种子高光谱图像及900~1 700 nm范围的光谱信息，建立了主成分分析法（PCA）-支持向量机（SVM）分类模型。运用PCA对光谱数据进行降维处理，结合SVM模型比较了不同实验条件下小麦种子分类准确率以及在最佳条件下3个、4个和6个品种种子的分类准确率。结果显示，3个品种间种子分类准确率除个别外平均达到95%以上，4个品种间种子分类准确率在80%左右，6个品种间种子分类准确率在66%左右。这说明充分利用光谱信息可以对3个或4个小麦品种进行多籽粒分类。 |
| 英文摘要: |
| In order to apply the hyperspectral imagine technology in classification and recognition of wheat seed,hyperspectral images and spectral information in the range of 900-1 700 nm from wheat seed of seven varieties were collected and extracted in Henan province.A principal component analysis(PCA)-support vector machine(SVM) classification model was established.The spectral data was processed by reducing dimension based on PCA,classification accuracy in different experimental conditions and its optimized classification accuracy in three,four and six varieties were compared by combining with SVM model.The experiment results showed that the average classification accuracy among the three different varieties is above 95% except for some individuals.The classification accuracy among the four varieties is about 80%.The classification accuracy among the six varieties is about 66%.The results showed that it is effective and feasible for multi-grain classification of three or four wheat seed varieties by spectral information. |
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