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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Guillaume Grégoire & Josée Fortin & Isa Ebtehaj & Hossein Bonakdari, 2023. "Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques," Agriculture, MDPI, vol. 13(6), pages 1-22, May.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1163-:d:1159933
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    References listed on IDEAS

    as
    1. Hossein Bonakdari & Isa Ebtehaj & Pijush Samui & Bahram Gharabaghi, 2019. "Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3965-3984, September.
    2. Guillaume Grégoire & Josée Fortin & Isa Ebtehaj & Hossein Bonakdari, 2022. "Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses," Agriculture, MDPI, vol. 12(7), pages 1-19, June.
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