Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
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, -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.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8979129 | PMC |
http://dx.doi.org/10.1039/d1ra06905e | DOI Listing |
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