An improved DCGAN model: Data augmentation of hyperspectral image for identification pesticide residues of Hami melon

Abstract

The increasing concern over pesticide residues on Hami melon is due to the unregulated use of pesticides, which poses a potential food safety hazard. Thus, it is urgent to propose a method for the rapid and nondestructive detection of pesticide residues on the Hami melon. This study used short-wave infrared hyperspectral imaging (SWIR-HSI) to identify pesticide residues on the Hami melon. The data augmentation method based on improved deep convolutional generative adversarial networks (DCGAN) was proposed to expand Hami melon's spectral data with different pesticide residues. To determine the optimal training epoch, the 1-nearest neighbor (1-NN) classifier was used to evaluate the quality of the generated spectra. The effectiveness of the improved DCGAN was verified by three commonly used classifiers, including the decision tree (DT), random forest (RF), and support vector machine (SVM). The results showed that the performance of all three classifiers was improved to varying degrees by the improved DCGAN. The DT, RF, and SVM accuracy was improved by 13.13%, 7.50%, and 11.25%, respectively. Moreover, the SVM model achieved the highest accuracy of 93.13%. These findings indicated that the combination of SWIR-HSI and the improved DCGAN-based data augmentation method has good promise for detecting pesticide residues on Hami melon.