## 1 Introduction
The potential application of VHSR data spans across the diverse aspects of vegetation observation [17], and provides continuous spatio-temporal information for characterizing plant conditions [18; 19; 20]. It is known that plants will exhibit specific host-pathogen interactions while under different infestations, such as, reductions in leaf area, variation in pigment content, and destruction of canopy morphology [21]. These effects can be obtained by continual spectral responses in the visible and near-infrared bands from multi-temporal observations [22; 23]. A number of broadband vegetation indices have been developed to capture plant biophysical, structural, and physiological parameters based on spectral analysis at the canopy scale [24; 25; 26; 27]. [PERSON] et al. [23] successfully developed a multi-temporal, modified soil-adjusted vegetation index (MSR) on the HJ-CCD images for detecting and mapping armyworm outbreak in Tangshan, Hebei Province of China. [PERSON] et al. [28] assessed the performance of the most suitable multi-spectral vegetation indices, GNDVI and NDVI, using Quickbird data for monitoring yellow rust disease in winter wheat. The results produced an acceptable accuracy for mapping yellow rust damage caused by disease. Apart from the direct utilization of vegetation indices, various automatic or semi-automatic methods have exhibited potential for these statistical learning-based models in agricultural research [29; 30; 31; 32]. [PERSON] et al. [33] used IKONOS imagery to test the performance of Support Vector Machines, Artificial Neural Networks (non-parametric methods), Mahalanobis Distance, and Maximum Likelihood (parametric methods) in damage detection, and to map the spread of an aggressive invasive alien species in the Azorean Laurel Forest. [PERSON] et al. [34] assessed the effectiveness of using SPOT-5 10-m multispectral imagery and a logistic regression model to detect and map red-attack damage for an area near Cranbrook, British Columbia, Canada. These studies have demonstrated that a pixel-based method can be used to detect symptoms induced by stresses using VHSR data. However, the opportunities for the use of VHSR satellite data and methods for discrimination of multiple disease species have rarely been explored. This is due to the fact that selecting spectral features and their inherent multicollinearity can overwhelm analytical methods and create difficulties when determining the best approach for detecting crop diseases. For example, [PERSON] et al. [35] examined the potential of multi-spectral remote sensing for a multi-temporal analysis of crop diseases, and used mixture tuned matched filtering (MTMF) on the results. Additionally, they used the NDVI calculated from QuickBird images to classify different species of leaf rust and powdery mildew. While successful, their approach was only moderately suitable for disease detection due to the homogeneity of the selected spectral features. Therefore, it is essential to develop a novel classification method that can fully address the similarity in spectral features among different infestations. Partial least squares (PLS) analysis is an efficient multivariate statistical technique that addresses the complexities associated with multicollinearity by simultaneously executing principle component extraction and classification [36; 37; 38]. However, PLS alone does not provide insight on the most sensitive features that may contribute to a final classification. Researchers have shown that pre-selecting features based on the variable importance in the projection (VIP) score serves as an apt measure for determining feature importance [39; 40]. Furthermore, selection of important spectral features has been observed to improve model performance [41]. To our knowledge, there are very few studies that assess the utility and potential of PLS for regression and classification.