Hyperspectral imaging combined with deep learning for discrimination of Lingwu long jujube in terms of the time after bruising

Abstract

The timely detection and classification of bruised Lingwu long jujube is crucial for reducing economic losses in the agricultural industry. Traditional machine learning methods had been effectively used for bruise time classification with limited samples while the challenges were presented when the amount of data expanded. Deep learning methods can offer a solution. The hyperspectral image of the jujube sample was obtained using near-infrared hyperspectral imaging system (900–1700 nm). The optimal input variables were obtained by comparing the performance of the partial least squares-discriminant analysis, artificial neural network, and one-dimensional convolutional neural network (1D-CNN) models based on the spectral data and the fusion data of spectral and textural features. The optimal variable was the fusion data that could improve the model performance (from 92.68% to 96.10%). The above-mentioned three classifiers were used to establish models based on fusion data for two different dataset sizes consisting of large datasets (LD) (820) and small datasets (SD) (200) to explore the influence of dataset size on model performance. As the sample size increased, the 1D-CNN had good robustness with a classification rate of 94.00% for SD and 96.10% for LD in the prediction sets for all samples. The classification rate of prediction sets for 0 h after bruising increased from 90.00% to 92.68%, and from 80.00% to 90.24% in the class of 24 h after bruising. The findings provided a new approach to building a large spectral library and developing an online classification and detection system, thereby providing new ideas for the development of grading systems for Lingwu long jujube.