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首页> 外文会议>International symposium on artificial intelligence and robotics >The Increasing of Discrimination Accuracy of Waxed Apples Based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis
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The Increasing of Discrimination Accuracy of Waxed Apples Based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis

机译：基于光谱相关分析优化的基于高光谱成像的蜡苹果辨别精度的增加

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To increase the classification accuracy and stability of the prediction model, an approach to evaluate the quality of samples' hyperspectral image is needed. The spectral correlation analysis of each pixel was used to determine quality of the sample's hyperspectral image in this study. 400 hyperspectral image ROIs were extracted from 20 apples (10 apples with waxed and the other 10 apples without any waxed) and the data were separated into 300 as train set and 100 as test set randomly. The experimental group data were evaluated by the spectral correlation analysis, and only qualified data were used for model training. The control group data were all used for modeling training. The least squares support vector machine (LS-SVM) model were used to establish the classification model between the hyperspectral image and waxed situation. The prediction result showed the classification accuracy were 94% and 86% when the low-quality sample data for training were filtered by spectral correlation analysis. By evaluating the quality of the hyperspectral image measured, more reliable prediction results can be obtained, which can make the noninvasive discrimination of food safety come to the practice application sooner.
机译：为了提高预测模型的分类精度和稳定性，需要一种评估样本高光谱图像的质量的方法。每个像素的光谱相关性分析用于确定本研究中样本的高光谱图像的质量。从20个苹果中提取400高光谱图像ROI（10苹果，其中10个苹果，没有任何蜡的其他10个苹果），并且将数据分成300作为列车组，随机设置为试验设置。通过光谱相关分析评估了实验组数据，只使用合格数据进行模型培训。控制组数据都用于建模培训。最小二乘支持向量机（LS-SVM）模型用于建立高光谱图像和蜡状情况之间的分类模型。当通过光谱相关分析过滤培训的低质量样本数据时，预测结果显示分类精度为94％和86％。通过评估测量的高光谱图像的质量，可以获得更可靠的预测结果，这可以使食品安全的非侵入性辨别迅速来到实践申请。

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