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首页> 外文期刊>Journal of Hazardous Materials >Predicting pesticide dissipation half-life intervals in plants with machine learning models
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Predicting pesticide dissipation half-life intervals in plants with machine learning models

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

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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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