A Comparison of Regression Tree Approaches to Modelling the Efficacy of Water Hyacinth Biocontrol Using Multitemporal Spectral Datasets

[摘要] Water hyacinth (Eichhornia crassipes) is an exotic plant species that is effectively controlled by Neochetina spp. 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.

[学科分类] 光谱学