Abstract

Pesticide residues enter a lake through the water cycle, causing harm to the water environment and human health. It is necessary to select highly sensitive fluorescence spectroscopy to detect pesticides (bifenthrin, prochloraz, and cyromazine), and a support vector machine (SVM) is used to analyze the concentration of pesticides. In addition, this paper adopts K-fold cross validation and a grid search to optimize the SVM algorithm. The performance evaluation index and running time prove the reliability of the results of this experiment. They show that fluorescence spectroscopy combined with SVM is efficient in predicting pesticide residue content.

© 2021 Optical Society of America

Detection of captan residues in apple juice using fluorescence spectroscopy combined with a genetic algorithm and support vector machines

Rendong Ji, Zhezhen Jiang, Xiaoyan Wang, Yue Han, Haiyi Bian, Yudong Yang, Liyun Zhuang, and Yulin Zhang
Appl. Opt. 61(12) 3455-3462 (2022)

Multiple kinds of pesticide residue detection using fluorescence spectroscopy combined with partial least-squares models

Rendong Ji, Shicai Ma, Hua Yao, Yue Han, Xiao Yang, Ruiqiang Chen, Yinshang Yu, Xiaoyan Wang, Dongyang Zhang, TieZhu Zhu, and Haiyi Bian
Appl. Opt. 59(6) 1524-1528 (2020)

Mixed pesticide recognition based on three-dimensional fluorescence spectroscopy and a convolutional neural network

Xiaoyan Wang, Xu Chen, Rendong Ji, Tao Wang, Ying He, Haiyi Bian, Xuyang Wang, and Wenjing Hu
Appl. Opt. 62(34) 9018-9027 (2023)

Non-contact fluorescent detection of pesticide residues based on segment prediction using PLS and a curve fitting algorithm

Yuanxue Cai and Haiyi Bian
Appl. Opt. 61(13) 3877-3883 (2022)

Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique

Caio Bruno Wetterich, Ruan Felipe de Oliveira Neves, José Belasque, and Luis Gustavo Marcassa
Appl. Opt. 55(2) 400-407 (2016)