A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

Lydia, M.; Edwin, Lydia

doi:10.1007/398_2020_64

Cited by 4 publications

(2 citation statements)

References 139 publications

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“…Vaz et al [3] were able to develop three new molecules effective against plant diseases by using ML on a training set of molecules with known activity. In a review study on the use of prediction models for pesticide use, Gilbert and Edwin [4] proposed different models based on deep learning to predict pesticide use on the basis of the occurrence of pests or past pesticide use. Different ML techniques have also been used to identify pesticide residues on fruits and vegetables [5,6] and to evaluate the toxicity of pesticides against non-target organisms [7,8].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

et al. 2023

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In the current study, a new hybrid machine learning (ML)-based model was developed by integrating a convolution neural network (CNN) with a random forest (RF) to forecast pesticide use on golf courses in Québec, Canada. Three main groups of independent variables were used to estimate pesticide use on golf courses, expressed as actual active ingredient rate (AAIR): (i) coordinates (i.e., longitude and latitude of the golf course), (ii) characteristics of the golf courses (i.e., pesticide type and the number of holes), and (iii) meteorological variables (i.e., total precipitation, P, and average temperature, T). The meteorological variables were collected from the Google Earth Engine by developing a JavaScript-based Code. On the basis of the different periods of total precipitation and average temperature, four different scenarios were defined. A data bank with more than 40,000 samples was used to calibrate and validate the developed model such that 70% of all samples were randomly selected to calibrate the model, while the remainder of the samples (i.e., 30%) that did not have any role in calibration were employed to validate the model’s generalizability. A comparison of different scenarios indicated that the model that considered the longitude and latitude of the golf course, pesticide type, and the number of holes in golf courses as well as total precipitation and average temperature from May to November as inputs (R = 0.997; NSE = 0.997; RMSE = 0.046; MAE = 0.026; NRMSE = 0.454; and PBIAS (%) = −0.443) outperformed the other models. Moreover, the sensitivity analysis result indicated that the total precipitation was the most critical variable in AAIR forecasting, while the average temperature, pesticide types, and the number of holes were ranked second to fourth, respectively.

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Section: Introductionmentioning

confidence: 99%

Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

et al. 2023

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“…The development of toxicity prediction models for optimal pesticide use, pesticide management and exposure is of great help in monitoring and controlling the harmful effects of pesticide overuse [9][10][11]. Toxicity increases with lipophilicity, as generally higher lipophilicity facilitates the passage of the substance across lipid cell membranes, the bloodbrain barrier and protein binding [12].…”

Section: Introductionmentioning

confidence: 99%

Calculation of Lipophilicity of Organophosphate Pesticides Using Density Functional Theory

et al. 2022

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Higher lipophilicity facilitates the passage of a substance across lipid cell membranes, the blood–brain barrier and protein binding, and may also indicate its toxicity. We proposed eight methods for predicting the lipophilicity of the 22 most commonly used organophosphate pesticides. In this work, to determine the lipophilicity and thermodynamic parameters of the solvation of pesticides, we used methods of density functional theory with various basis sets, as well as modern Grimm methods. The prediction models were evaluated and compared against eight performance statistics, as well as time and RAM used in the calculation. The results show that the PBE-SVP method provided the best of the proposed predictive capabilities. In addition, this method consumes relatively less CPU and RAM resources. These methods make it possible to reliably predict the ability of pesticide molecules to penetrate cell membranes and have a negative effect on cells and the organism as a whole.

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Mechanisms of adsorption and functionalization of biochar for pesticides: A review

Dong,

Chu,

Tong

et al. 2024

Ecotoxicology and Environmental Safety

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A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

Cited by 4 publications

References 139 publications

Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

Calculation of Lipophilicity of Organophosphate Pesticides Using Density Functional Theory

Mechanisms of adsorption and functionalization of biochar for pesticides: A review

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