A two-stage framework for detection of pesticide residues in soil based on gas sensors
Chinese Journal of Analytical Chemistry, ISSN: 1872-2040, Vol: 50, Issue: 11, Page: 100124
2022
- 6Citations
- 21Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The analysis of pesticide residues in soil is essential for the ecological environment, food safety and human health. Focused on the real time detection of pesticide residues in soil by using E-nose technique, this paper proposes a two-stage framework to identify the categories and concentrations of pesticide residues. This method is based on multi-task learning and transfer learning, which improves the generalization ability of the model. Experimental results show that the proposed method outperforms the comparing existed methods, and verify the effectiveness of two-stage stratege and transfer learning as well. It can be potentially applied to the in situ identification of soil pesticide residues, which is of great significance for soil improvement and agricultural production.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1872204022000792; http://dx.doi.org/10.1016/j.cjac.2022.100124; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85134192230&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1872204022000792; https://dx.doi.org/10.1016/j.cjac.2022.100124
Elsevier BV
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