# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 5 Conclusions  
In this study, an electronic nose device was utilized to collect odor samples from different types of pesticide-contaminated soils. A hybrid model combining unsupervised and supervised learning approaches was proposed. Among the detection methods for soil pesticide residue contamination, the combination of the one-class SVM and the _Mean_ achieved the shortest detection time and a recognition rate of 99.30%. In the module for detecting the types of pesticide residue contamination in soils, the combination of the _Mean_ and SVM achieved the highest recognition rate of 99.27%. Overall, using an electronic nose system and the _Mean_ as a feature extraction method resulted in the optimal recognition accuracy and the fastest detection time. This demonstrates the ability of the electronic nose system to collect soil odor information and, when combined with feature extraction and machine learning methods, to effectively identify whether the soil is contaminated with a pesticide residue and the type of residue. Moreover, it can quickly identify soil pesticide residue even with a small sample size during the precise soil remediation process.  
Conceptualization, J.Q. and Y.L.; methodology, J.Q., Y.F. and X.W.; investigation, Y.F. and J.L.; supervision, Y.Z. and S.L.; writing--original draft, J.Q. and Y.L.; writing--review and editing, C.L. and X.W.; validation, Y.L., Y.Z. and J.L.; software, Y.L.; visualization, Y.Z., J.L. and S.L.; supervision, Y.Z. and S.L.; project administration, C.L. and X.W.; resources, J.Q. and X.W.; formal analysis, Y.F. and C.L.; data curation, Y.F. and S.L.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (U22A20184), the Science-Technology Development Plan Project of Jilin Province (YDZJ202101 ZYTS003, 202102030045F, 20230203149 SF, 20230201069 CX), and the Special Project of Industrial Technology Research and Development of Jilin Province (2022C045-6).  
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number can be found in the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application Scenario** & **Pesticide Brands** & **Recognition Rates** & **References** \\ \hline Tea & Cyhalothrin; bifenthrin; fenpropothrin & \textgreater{}88\% & [37] \\ \hline Cherries & Diazmon & \textgreater{}100\% & [27] \\ \hline Apples & Cypermethrin; chlorpyrifos & \textgreater{}94.64\% & [26] \\ \hline Chili & Profenofos & / & [38] \\ \hline Mint & Malathion & \textgreater{}97\% & [39] \\ \hline Soil & Glyphosate; chlorpyrito; deltamethrin; cyfluthrin; & \textgreater{}92.5\% & [23] \\  & mancozek; dithane z-78 & \textgreater{}93.75\% & [24] \\ \hline Soil & Chlorpyrifos; cyfluthrin; dithane & \textgreater{}98.08\% & [40] \\ \hline Groundwater & Glyphosate; chlorpyrifos; deltamethrin; cyfluthrin; & \textgreater{}98.08\% & [40] \\  & mancozek; zineb & \textgreater{}99.29\% & [41] \\ \hline \hline \end{tabular}
\end{table}
Table 5: The summary of papers on the application of electronic noses in pesticide detection.