A Comparison of Regression Tree Approaches to Modelling the Efficacy of Water Hyacinth Biocontrol Using Multitemporal Spectral Datasets
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Published:2018
Issue:
Volume:2018
Page:1-11
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ISSN:2314-4920
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Container-title:Journal of Spectroscopy
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language:en
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Short-container-title:Journal of Spectroscopy
Author:
Agjee Na’eem Hoosen1ORCID,
Mutanga Onisimo1,
Gebreselasie Michael1,
Ismail Riyad1
Affiliation:
1. School of Agriculture, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01 Scottsville, Pietermaritzburg 3209, South Africa
Abstract
Water hyacinth (Eichhornia crassipes) is an exotic plant species that is effectively controlled byNeochetinaspp. weevils. This study is aimed at determining if spectroscopic data may be utilized to predict insect-induced stress on water hyacinth plants. Single target regression trees (STRTs), multitarget regression trees (MTRTs), and random forest multitarget regression trees (RF-MTRTs) have been used to predict feeding scar damage (FSD) and relative leaf chlorophyll content (RLCC) from hyperspectral canopy reflectance data. Results from this study show that the correlation coefficient of STRTs (training accuracy: 76%–97%; validation accuracy: 47%–86%) performs better than MTRTs (training accuracy: 74%–90%; validation accuracy: 45%–77%) for all infestation levels but are difficult to interpret simultaneously. In contrast, MTRTs (size: 23–35 nodes) are much smaller and more interpretable than STRTs (size: 11–47 nodes) because they predict FSD and RLCC simultaneously. Importantly, RF-MTRTs (training accuracy: 95%–98%; validation accuracy: 55%–88%) yield better predictive performance than STRTs and MTRTs for all infestation levels. It is concluded that MTRTs can be utilized for model interpretation as they are more interpretable; however, RF-MTRTs offer an improved predictive performance.
Funder
National Research Foundation
Publisher
Hindawi Limited
Subject
Spectroscopy,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
1 articles.
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