Predicting water stress induced by Thaumastocoris peregrinus infestations in plantation forests using field spectroscopy and neural networks

Field spectroscopy was tested in predicting water stress induced by Thaumastocoris peregrinus infestations, a pest which is causing significant damage to eucalypt plantations internationally. Water indices and known water absorption bands calculated from hyperspectral field reflectance were input into a neural network algorithm to predict plant water content (PWC) and equivalent water thickness (EWT). The integrated approach involving field spectral data and neural networks predicted PWC and EWT with correlation coefficients of 0.88 and 0.71 on independent test datasets. The results indicate the potential of high-resolution field spectral data in detecting the early stages of insect infestation due to physiological changes that alter water content.

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