Journal
JOURNAL OF HAZARDOUS MATERIALS
Volume 436, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jhazmat.2022.129177
Keywords
Machine Learning; Pesticide; Dissipation half-life; Extended connectivity fingerprints; Molecular structure; Gradient boosting regression tree
Categories
Funding
- National Institute of Environmental Health Sciences, United States [R35ES031688, P30ES009089]
- Hatch Act Formula Grant from USDA National Institute of Food and Agriculture, United States [1012794, 1021038]
- National Natural Science Foundation of China, China (NSFC) [31301694]
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Pesticide dissipation half-life in plants is crucial for assessing environmental fate of pesticides and establishing good agriculture practices. This study utilized machine learning models to predict the dissipation half-life and proposed novel intervals to account for variations in empirical data. The best-performing model, GBRT-ECFP, showed the potential of machine learning in evaluating pesticide fate in agricultural crops.
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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