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Predicting pesticide dissipation half-life intervals in plants with machine learning models

文献类型： 外文期刊

作者： Shen, Yike 1 ; Zhao, Ercheng 2 ; Zhang, Wei 3 ; Baccarelli, Andrea A. 1 ; Gao, Feng 1 ;

作者机构： 1.Columbia Univ, Mailman Sch Publ Hlth, Dept Environm Hlth Sci, New York, NY 10032 USA

2.Inst Plant Protect, Beijing Acad Agr & Forestry Sci, Beijing 100097, Peoples R China

3.Michigan State Univ, Dept Plant Soil & Microbial Sci, E Lansing, MI 48823 USA

4.Michigan State Univ, 1066 Bogue ST RM A516, E Lansing, MI 48824 USA

5.Columbia Univ Mailman Sch Publ Hlth, Dept Environm Hlth Sci, 630 W168th St Room 16-416, New York, NY 10032 USA

关键词： Machine Learning; Pesticide; Dissipation half-life; Extended connectivity fingerprints; Molecular structure; Gradient boosting regression tree

期刊名称：JOURNAL OF HAZARDOUS MATERIALS （ 影响因子：14.224； 五年影响因子：12.984 ）

ISSN： 0304-3894

年卷期： 2022 年 436 卷

页码：

收录情况： SCI

摘要： Pesticide dissipation half-life in plants is an important factor to assessing environmental fate of pesticides and establishing pre-harvest intervals critical to good agriculture practices. However, empirically measured pesticide dissipation half-lives are highly variable and the accurate prediction with models is challenging. This study utilized a dataset of pesticide dissipation half-lives containing 1363 datapoints, 311 pesticides, 10 plant types, and 4 plant component classes. Novel dissipation half-life intervals were proposed and predicted to account for high variations in empirical data. Four machine learning models (i.e., gradient boosting regression tree [GBRT], random forest [RF], supporting vector classifier [SVC], and logistic regression [LR]) were developed to predict dissipation half-life intervals using extended connectivity fingerprints (ECFP), temperature, plant type, and plant component class as model inputs. GBRT-ECFP had the best model performance with F1-microbinary score of 0.698 +/- 0.010 for the binary classification compared with other machine learning models (e.g., LR-ECFP, F1-micro binary= 0.662 +/- 0.009). Feature importance analysis of molecular structures in the binary classification identified aromatic rings, carbonyl group, organophosphate, =C-H, and N-containing heterocyclic groups as important substructures related to pesticide dissipation half-lives. This study suggests the utility of machine learning models in assessing the environmental fate of pesticides in agricultural crops.

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