# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 3 Results and Analysis
### Unsupervised Soil Contamination Test Results  
Each detection model was trained using 40 samples and health testing was conducted on 1000 samples. The results of the soil pesticide residue pollution detection are shown in Table 3. As shown in Table 3, in the soil pollution detection module, the recognition rates of _FT_, _MAX_, _Mean_, and _WT_ combined with the one-class SVM have all reached over 99.00%, with the highest recognition rate being that of the _WT_, reaching 99.40%, and the lowest being that of the _FT_, reaching 99.00%. This indicates that a one-class SVM machine combined with feature extraction can not only extract soil contamination information, but also can use the extracted soil contamination information to effectively distinguish whether the soil is contaminated or not. One-class SVMs only require healthy soil samples during the training process of pollution detection models, learning the rules of healthy samples, and do not require additional pollution samples. While the current, commonly used, supervised learning classifier can slightly outperform the one-class SVM in some cases, it needs healthy soil samples and pesticide-contaminated soil samples for pollution detection. However, in most cases, it is difficult to obtain training samples covering all pesticide types, and the accuracy of the established model in detecting newly measured soil pollution samples will be greatly affected in practical applications. Moreover, with the reduction in the pollution sample size during model training, it can be found that the effect of supervised learning on soil health detection will decline, becoming even lower than that of the one-class SVM. This is because as the number of contaminated samples decreases, the pollution feature information is greatly reduced, which will reduce the machine's recognition ability for contaminated samples. Figure 4 lists the confusion matrix of each detection model established using the _MAX_ eigenvalue. From the confusion matrix, it can be seen that once the monitoring model identifies errors, it will misjudge the contaminated samples to be healthy samples. This will prevent the contaminated soil from being discovered, resulting in its continuous pollution and deterioration, which is not conducive to soil remediation and management. However, the one-class SVM often misjudge healthy samples as contaminated samples when identifying errors. As a preliminary screening method for soil pollution, the electronic nose will perform more accurate measurements on predicted pollution samples after identification, so the impact of this misjudgment on soil governance and remediation is minimal. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Training Samples**} & **Healthy** & **Polluted** & **Healthy** & **Polluted** & **Healthy** & **Polluted** \\  & **Sample** & **Sample** & **Sample** & **Sample** & **Sample** & **Sample** \\ \cline{2-7}
**Detection Model** & **20** & **20** & **30** & **10** & **40** & **0** \\ \hline KNN-FT & 100.0 & & 100.0 & & - \\ RF-FT & 99.9 & & 97.6 & & - \\ SVM-FT & 99.9 & & 98.7 & & - \\ One-class SVM-FT & - & & - & & 99.0 \\ KNN-MAX & 100.0 & & 100.0 & & - \\ RF-MAX & 99.9 & & 99.5 & & - \\ SVM-MAX & 99.0 & & 98.6 & & - \\ One-class SVM-MAX & - & & - & 99.2 \\ KNN-Mean & 100.0 & & 100.0 & & - \\ RF-Mean & 99.9 & & 99.6 & & - \\ SVM-Mean & 98.7 & & 100.0 & & - \\ One-class SVM-Mean & - & & - & 99.3 \\ KNN-WT & 100.0 & & 100.0 & & - \\ RF-WT & 100.0 & & 99.2 & & - \\ SVM-WT & 97.6 & & 100.0 & & - \\ One-class SVM-WT & - & - & - & 99.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Soil pesticide residue pollution test results. Meanwhile, the electronic nose system applied in this paper performs real-time detecting of soil pollution.