Detection of Subtle Bruises on Winter Jujube Using Hyperspectral Imaging With Pixel-Wise Deep Learning Method

Abstract

Winter jujubes get bruised easily during harvest and transportation. In order to detect subtle bruises on winter jujubes in a more efficient way, a rapid and accurate technique, hyperspectral imaging was used. Near-infrared reflectance (NIR) and visible/near-infrared reflectance (Vis-NIR) hyperspectral imaging at the spectral region of 874-1734 nm and 380-1030 nm, respectively, were applied in this study. The hyperspectral images of winter jujubes from four geographical origins were acquired. Pixel-wise spectra were extracted and preprocessed; pixel-wise principal component analysis (PCA) was used to conduct a qualitative analysis. Accuracy, true positive rate (TPR) and false positive rate (FPR) were utilized to compare the efficiency of the models. Support vector machine (SVM), logistic regression (LR) and a deep learning method, and convolutional neural network (CNN) were used to build pixel-wise classification models based on single or all geographical origins for quantitative analyses. All the models using NIR spectra obtained decent results with accuracies in the range of 90-100%, and TPRs and FPRs close to 1 and 0, respectively. Compared with the other two methods using Vis-NIR spectra, the CNN model based on all geographical origins got the best performance with most of the accuracies surpassing 85%. For Vis-NIR spectra and NIR spectra, the overall time efficiency for modeling and prediction of CNN was at an intermediate level among the three models. The short prediction time of CNN indicated that CNN had the potential for real-time detection. The prediction maps obtained by the CNN models indicated that the color information and geographical origins could affect the detection performance. The overall results demonstrated the promising potential for detecting subtle bruises on winter jujubes using pixel-wise spectra extracted from the hyperspectral images at the two spectral ranges with the deep learning method. The results in this study would help to develop an online winter jujube bruises detection system in the future.

Keywords

• Winter jujubes
• hyperspectral imaging system
• support vector machine
• logistic regression
• convolutional neural networks