Predicting plant disease epidemics using boosted regression trees

Plant epidemics are often associated with weather-related variables. It is difficult to identify weather-related predictors for models predicting plant epidemics. In the article by Shah et al., to predict Fusarium head blight (FHB) epidemics of wheat, they explored a functional approach using scalar...

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Published in:Infectious disease modelling 2024-12, Vol.9 (4), p.1138-1146
Main Authors: Peng, Chun, Zhang, Xingyue, Wang, Weiming
Format: Article
Language:English
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Boosted regression trees
Plant disease epidemics
Scalar-on-function model
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description Plant epidemics are often associated with weather-related variables. It is difficult to identify weather-related predictors for models predicting plant epidemics. In the article by Shah et al., to predict Fusarium head blight (FHB) epidemics of wheat, they explored a functional approach using scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) with respect to weather time series spanning 140 days relative to anthesis. The scalar-on-function models fit the data better than previously described logistic regression models. In this work, given the same dataset and models, we attempt to reproduce the article by Shah et al. using a different approach, boosted regression trees. After fitting, the classification accuracy and model statistics are surprisingly good.
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subjects Boosted regression trees
Plant disease epidemics
Scalar-on-function model
title Predicting plant disease epidemics using boosted regression trees
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