Authors

Abstract

In order to perform highly effective identification of external defects and increase the additional value of Cerasus Humilis fruits, this study used hyperspectral imaging technology to collect information on intact and defective Cerasus Humilis fruits. Based on the full transition spectrum, partial least squares discriminant analysis (PLS-DA) and back propagation neural networks (BPNN) were used to establish a discriminative model. The competitive adaptive reweighted sampling (CARS) was used to extract feature wavelengths, principal component analysis was used for data compression of single band images, BPNN and convolutional neural networks (CNN) were used for defect Cerasus Humilis fruits recognition of principal component images. The results showed that the overall detection accuracy of PLS-DA and BPNN models based on wavelength spectral information were 83.81% and 85.71%, respectively. BPNN was used to establish the calibration model based on the selected characteristic wavelengths by CARS, the accuracy rate was 90.47%. The classified accuracy of CNN model based on principal component images was 93.33%, which was obviously better than that of BPNN model at 83.81%. The research shows that the CNN model was successfully applied to the detection of Cerasus Humilis fruits defects using hyperspectral imaging. This study provides a theoretical basis for the development of fruit grading and sorting equipment.

Abstract in Chinese

为实现欧李果缺陷特征的识别，提高欧李果附加价值，采用高光谱成像技术采集了完好和缺陷欧李果的信息。基于全渡段光谱，采用偏最小二乘判别分析(Partial Least Squares-Discriminant Analysis, PLS-DA)和误差反向传播神经网络(Back Propagation Neural Networks, BPNN)建立判别模型。采用竞争自适应加权(CARS)提取特征波长，并利用主成分分析进行单波段图像的数据压缩，针对主成分图像采用BPNN和卷积神经网络(Convolutional Neural Networks, CNN)进行缺陷欧李果识别。研究结果表明，基于主成分图像建立的CNN模型对缺陷欧李果的识别效果最好，其准确率为93.33%。该研究为开发水果的分级分选设备提供了理论基础。