基于高光谱成像技术的腐烂、病害梨枣检测

Detection of Decay and Disease Pear Jujube Based on Hyperspectral Imaging Technology

研究利用高光谱成像技术对腐烂、病害及正常梨枣进行分类。首先分析比对了多种预处理方法,确定使用一阶微分处理可得到最佳的建模效果。利用线性的逐步判别分析法和非线性的偏最小二乘支持向量机(LS-SVM)建立分类模型时,比较了全波段模型、近似系数模型和主成分模型的参数和预测效果。结果表明,使用光谱近似系数为特征参数并使用逐步判别分析法建立的模型得到了最佳的分类效果,其分类准确率达到了99.12%。

The classified feasibility of pear jujube with normal,rot and disease defect fruit by using hyperspectral imaging technology was analyzed.A best mathematical model was established by treating first derivative as the best pre-processing which was compared with other different kinds of proceeding methods.The stepwise discriminant analysis and least square support vector machines( LS-SVM) were applied to build the full-wave band, approximation coefficients and principal components models,respectively.The results indicated that the stepwise discriminant analysis model was more suitable for classifying the three different kinds of pear jujube samples.The average correct recognition rate was 99.12%.