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

Guillaume Grégoire (), Josée Fortin, Isa Ebtehaj and Hossein Bonakdari
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Guillaume Grégoire: Centre de Recherche et d’Innovation sur les Végétaux, Département de Phytologie, Université Laval, Québec, QC G1V 0A6, Canada
Josée Fortin: Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada
Isa Ebtehaj: Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, Canada
Hossein Bonakdari: Department of Civil Engineering, University of Ottawa, 161 Louis Pasteur Private, Ottawa, ON K1N 6N5, Canada

Agriculture, 2023, vol. 13, issue 6, 1-22

Abstract: 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.

Keywords: actual active ingredient rate (AAIR); convolution neural network (CNN); golf courses; hybrid model; random forest (RF); meteorological variables; pesticides; sustainable agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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