AI Article Synopsis
- Plant diseases can be affected by weather, but it's hard to find the right weather factors to predict them.
- In a study, researchers used a special method to predict a wheat disease called Fusarium head blight by looking at weather data.
- They found that their new method worked really well, so another team tried a different way using boosted regression trees and got good results too.
Article Abstract
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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11253225 | PMC |
| http://dx.doi.org/10.1016/j.idm.2024.06.006 | DOI Listing |
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