## 1 Introduction  
Rice seeds are known to harbor endophytes along with numerous seedborne bacterial and fungal pathogens that can decrease plant stands in production fields and limit yield [1; 2]. One example of this is bacterial panicle blight (BPB), which is caused by the bacterium _Burkholderia glumae_. BPB is a globally important disease of rice, particularly in tropical and sub-tropical climates, and can lead to 75% yield loss in severely infested fields [3; 4]. BPB is largely seedborne, with the pathogen colonizing the growing plant and causing disease symptoms to appear after the heading stage. Infected panicles have high sterility and blighted kernels that have dark-brown margins on the glumes [5]. One way of reducing the incidence of BPB is to use uninfected seed for field planting. However, efforts to control the disease have been hindered by the lack of effective chemical control and few sources of genetic resistance being identified [6]. Although there are a few reports of quantitative trait loci being associated with improved resistance to the disease, breeding for resistance has been hindered by the lack of adapted germplasm, the difficulty of obtaining effective inoculations for disease screening, and the difficulty in quantifying disease symptoms [7; 8]. Furthermore, investigations into identifying and quantifying incidence of BPB disease symptoms have been made, but information is still largely lacking. Therefore, subjective visible assessment of panicles in the field or using post-harvested seeds for development of the discoloration and distinctive BPB symptoms is currently the only means to quantify incidence of the disease. Hyperspectral imaging (HSI) has been used for assessment fungal infection levels in rice panicles, which was previously performed by human visual surveys [9; 10]. These subjective observations are tedious, time-consuming, and less accurate than HSI. Moreover, visual surveys for disease incidence severely limit the sample quantities that can be inspected. Development of a rapid and nondestructive technique to accurately assess disease incidence in seed would enhance disease control research efforts and offer a means of high-throughput sorting of seed to assure healthy seed rice for planting, to prevent spread of the disease, and to assure plant stand establishment in fields. A variety of machine vision technologies, such as magnetic resonance and Raman and thermal imaging, are being used to aid in quality control of food products. Among them, visible (VIS) and near-infrared (NIR) HSI provides spectra and digital image (morphology) information. Moreover, HSI can provide more accurate color information than a common RGB camera that uses just red, green and blue wavelengths with broad waveband resolution, since HSI has higher spectral resolution (narrow wavebands) and can use hundreds of continuous wavelengths [11]. For example, a recent study showed the limitations of RGB cameras to differentiate disease severity levels compared to a multispectral imaging method that provided different levels of sheath blight symptoms in field plots using specific spectral information [12]. Furthermore, [PERSON] et al. (2017) [13], evaluated the accuracy of color measurements for tomato ripeness stages via a VIS-NIR HSI system. In a similar study using VIS-NIR HSI systems, [PERSON] et al. (2013) [14], developed a model based on color information for classification of six representative serogroups on agar plates. The results of these studies suggest that an efficient sorting machine for disease-infected seeds based on a VIS-NIR HSI system should be feasible since it can detect the most obvious feature of BPB infected rice, color change of the kernel. However, for practical use, the high spectral dimension of hyperspectral images must be reduced and a few optimal wavelengths selected to reduce the data processing load [15].