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

Author:

Qiao Jianlei1,Lv Yonglu1,Feng Yucai1,Liu Chang2,Zhang Yi1,Li Jinying1,Liu Shuang1,Weng Xiaohui3

Affiliation:

1. College of Horticulture, Jilin Agricultural University, Changchun 130118, China

2. College of Medical Information, Changchun University of Chinese Medicine, Changchun 130118, China

3. School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

Abstract

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.

Funder

National Natural Science Foundation of China

Science-Technology Development Plan Project of Jilin Province

Special Project of Industrial Technology Research and Development of Jilin Province

Publisher

MDPI AG

Reference41 articles.

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