高光谱图像对白萝卜糠心的无损检测

为实现白萝卜异常品质糠心的无损检测,构建高光谱图像技术检测白萝卜糠心的检测系统。获取了光源透射、反射和半透射模式下白萝卜的高光谱图像信息,结合偏最小二乘分析(partial least squares discriminant analysis,PLS-DA)、支持向量机(support vector machine,SVM)、人工神经网络(artificial neural network,ANN)3种算法分别建立白萝卜糠心的识别模型。结果表明:3种检测模式中,基于透射模式的高光谱图像系统检测准确率最高;3种预测模型中,ANN模型优于PLS-DA和SVM模型。其中,基于透射模式的ANN模型,高光谱图像对萝卜糠心的检测总体准确率达94.3%,效果最好。因此,采用透射模式的高光谱图像技术对白萝卜糠心的检测是可行的。

Detecting Hollowness of White Radish Based on Hyperspectral Imaging

Hollowness is a common defect found in radish postharvest storage. In the present study, a prototype hyperspectral
imaging system was designed for evaluating the internal quality of white radish. Three different detection models including
semi-transmittance, reflectance and transmittance were evaluated and used to extract the hyperspectral imaging data of
white radish, partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and artificial neural
network (ANN) algorithms were then used to establish the hollowness model for radish identification and the recognition
accuracy was calculated. The prediction accuracies based on PLS-DA, SVM, and ANN were 72.5%, 72.5% and 83.3% in
semi-transmittance mode, 82.5%, 82.5% and 92.3% in reflectance mode, and 90.0%, 90.0% and 94.3% in transmittance
mode, respectively. The results showed that hyperspectral transmittance imaging achieved the best prediction results among
the three different detection models, ANN algorithm was the optimal algorithm to build hollowness discrimination model.
Hyperspectral transmittance imaging in the combination with ANN gave the best results with a prediction accuracy of 94.3%
for detecting the internal hollowness of white radish. Therefore, it was feasible to use hyperspectral transmittance imaging
system for detecting the hollowness of white radish in postharvest storage.