Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China.
Sensors (Basel). 2019 Jul 17;19(14):3147. doi: 10.3390/s19143147.
In this study, a hyperspectral imaging system of 866.4-1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky-Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC-NCA-SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC-NCA-SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.
在这项研究中，选择了一个 866.4-1701.0nm 的高光谱成像系统，并结合多元方法来识别表面有不同浓度氧乐果的小麦籽粒。为了获得最佳的模型组合，应用了三种预处理方法（标准正态变量（SNV）、Savitzky-Golay 一阶导数（SG1）和多元散射校正（MSC））、三种特征提取算法（连续投影算法（SPA）、随机青蛙（RF）和邻域成分分析（NCA））和三种分类器模型（决策树（DT）、k-最近邻（KNN）和支持向量机（SVM））进行比较。首先，基于全波长建模分析，发现 MSC 处理后的光谱数据在三种分类器模型中的表现最好。其次，采用三种特征提取算法提取 MSC 处理后数据的特征波长，并基于特征波长建模分析。结果表明，MSC-NCA-SVM 模型表现最好，被选为最佳模型。最后，为了验证所选模型的可靠性，将高光谱图像代入 MSC-NCA-SVM 模型，并使用面向对象的方法对图像进行分类可视化。四种类型的小麦籽粒的总体分类准确率达到 98.75%，表明所选模型是可靠的。
