A two-stage framework for detection of pesticide residues in soil based on gas sensors

Shi, Xiaohu; Qiao, Yuheng; Luan, Xiangyu; Yuan, Yuping; Xu, Lin; Chang, Zhiyong

doi:10.1016/j.cjac.2022.100124

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…Kong et al [23], inspired by bionics, proposed a biomimetic strategy for detecting unknown pesticide residues in soil using electronic noses. These findings indicate the broad application potential of electronic noses in soil pollution monitoring, including pesticide residue detection [24].…”

Section: Introductionmentioning

confidence: 68%

Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models

Qiao,

Lv,

Feng

et al. 2024

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At present, the electronic nose has became a new technology for the rapid detection of pesticides. However, the technique may misidentify them for samples that have not been involved in training. Therefore, a hybrid model based on unsupervised and supervised learning was proposed for the first time in this paper. The model divided the detection process of soil pesticide residues into two steps: (1) an unsupervised machine learning method was used to identify whether the soil was contaminated with pesticides; (2) when the soil was contaminated with pesticides, a supervised classifier was further used to predict the types of pesticides in the soil. The experimental results showed that the model had a recognition accuracy of 99.3% and 99.27% for whether the soil was contaminated with pesticides and the pesticide type of the contaminated soil, respectively, with a detection time of 0.03 s. The results revealed that the proposed hybrid model can quickly and comprehensively reflect the soil information’s status.

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Section: Introductionmentioning

confidence: 68%