Issue 3, 2022

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Abstract

Feature extraction is a key factor to detect pesticides using terahertz spectroscopy. Compared to traditional methods, deep learning is able to obtain better insights into complex data features at high levels of abstraction. However, reports about the application of deep learning in THz spectroscopy are rare. The main limitation of deep learning to analyse terahertz spectroscopy is insufficient learning samples. In this study, we proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and the residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy. The Wasserstein generative adversarial network and pretraining model technology were employed to solve the problem of insufficient learning samples for training the ResNet. The Wasserstein generative adversarial network was used for generating more new learning samples. At the same time, pretraining model technology was applied to reduce the training parameters, in order to avoid residual neural network overfitting. The results demonstrate that our proposed method achieves a 91.4% accuracy rate, which is better than those of support vector machine, k-nearest neighbor, naïve Bayes model and ensemble learning. In summary, our proposed method demonstrates the potential application of deep learning in pesticide residue detection, expanding the application of THz spectroscopy.

Graphical abstract: Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Article information

Article type
Paper
Submitted
15 Sep 2021
Accepted
20 Dec 2021
First published
11 Jan 2022
This article is Open Access
Creative Commons BY license

RSC Adv., 2022,12, 1769-1776

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

R. Yang, Y. Li, B. Qin, D. Zhao, Y. Gan and J. Zheng, RSC Adv., 2022, 12, 1769 DOI: 10.1039/D1RA06905E

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements