Title:Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment
Volume: 20
Issue: 1
Author(s): Yunfeng Yang, Junjie Zhong, Songyu Shen, Jiajun Huang, Yihan Hong, Xiaosheng Qu*, Qin Chen*Bing Niu*
Affiliation:
- National Engineering Laboratory
of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants,
Goang Xi, China
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
Keywords:
Machine learning, pesticide, toxicity assessment, algorithm, agricultural chemicals, fungicides.
Abstract: Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential
increase in the number of new pesticides being synthesized, it becomes more and more
important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing
data, machine learning methods can train and model the predictions of the effects of novel pesticides,
which have limited available data. Combined with other technologies, this can aid the synthesis of
new pesticides with specific active structures, detect pesticide residues, and identify their tolerable
exposure levels. This article mainly discusses support vector machines, linear discriminant analysis,
decision trees, partial least squares, and algorithms based on feedforward neural networks in machine
learning. It is envisaged that this article will provide scientists and users with a better understanding
of machine learning and its application prospects in pesticide toxicity assessment.
