## 1 Introduction  
_Sclerotinia_ stem rot (SSR) is considered as an important disease of major crops [1] and some medicinal plants worldwide. It is caused by a typical necrotrophic phytopathogenic fungus, _Sclerotinia sclerootomy_ (_S. sclerootomy_), which could induce significant crop yield loss in oilseed rape, peanut, sunflower [2], safflower [3] and perilla frutescens [4].The traditional detection methods for SSR are mainly based on artificial identification and laboratory biochemical methods including flow cytometry, polymerase chain reaction (PCR), enzyme-linked immune sorbent assay (ELISA) and immunofluorescence (IF) [5]. Although these methods could gain relatively accurate results, they are often time-consuming and labor-intensive. Therefore, a reliable and rapid diagnose method for SSR is urgently needed.  
Currently, optical techniques, especially hyperspectral imaging (HSI) has been widely used for plant diseases detection [6; 7; 8] due to the advantage of providing spectral and spatial information simultaneously which contain information of chemical compositions and physical structures of the plant. Several studies have reported that HSI was feasible for plant disease detection, such as _Plasmopara viticola_ in grape [9], green diseases in citrus [10], _Magnaporthe oryzae_ in rice [11], gray mold in tomato [12; 13]. As for SSR detection, one study conducted by [PERSON] et al. on SSR detection of oilseed rape leaves using mid-infrared spectrum provided an over 80% detection accuracy [14]. However, point to point or one dimensional (1D) measurement could cause high uncertainty in disease detection due to that the disease would cause large variability and heterogeneity from a single leaf to the entire plant. Considering the large heterogeneity of disease distributing within a leaf, two- or three-dimensional optical imaging techniques containing spectral and spatial information have been employed. [PERSON] et al. carried out the SSR identification in oilseed rape stems using hyperspectral imaging with an overall accuracy of over 90% [15]. Although a reasonable result was achieved, it still needed 13 selected wavelengths to train the discriminant model, which can limit laboratory-based studies to a low-cost application. To our knowledge, there is little information about changes in light absorption and scattering in leaf tissue caused by _S. scleroorum_ infection. Therefore, the model plant, _Arabidopsis thaliana_ (_A. thaliana_), was introduced in this study for the investigation of diffuse reflectance heterogeneity within a whole leaf infected by _S. scleroorum_. Moreover, the findings would enable the effective detection of SSR based on HSI.  
In the study, HSI was proposed for the investigation of SSR infected leaves, as well as detection of SSR by establishing a robust discriminant model based on hyperspectral imaging and multivariate analysis. To achieve this goal, different optimal wavelength selection methods and classification algorithms were evaluated in order to select the most optimal model that was feasible for SSR detection. The specific objectives were: (1) Select the optimal wavelengths for SSR detection; (2) establish a suitable classification model for SSR diagnosis based on selected features.