## 1 Introduction  
The sweetness of sweet corn is a major factor in consumer satisfaction, which attracts a high interest from breeders to breed sweeeter corn cultivars [1]. Unlike most other crops, sweet corn is mainly consumed when it is immature because of its nutrition and sweet flavor [2]. Some characteristic genes of sweet corn cultivars, such as sugary-1, sugary enhanced, and shrunken, can make the conversion of sugar to starch negligible before the kernels fully ripen [3]. For this reason, a uniform maturity time is crucial for farmers to choose the optimal harvest time, and then to obtain a proper shelf-life quality as the sweet flavor of sweet corn changes quickly after harvesting [4]. However, different cultivars of sweet corn will vary in the maturity cycle, even under the same planting conditions [5]. Hence, it is significant to develop methods for distinguishing the cultivars of sweet corn seeds and making the maturity time consistent. The purity of a seed cultivar is defined as the ratio of seeds belonging to a cultivar to the total tested seeds [6]. After removing other cultivars of sweet corn seeds, the tidy harvest time can be obtained with the pure cultivar seeds. Moreover, because the economic value, nutritional value, and resistance to diseases and pests of sweet corn are also related to the cultivar attributes [7], improving the purity of sweet corn seed can also ensure the quality and yield [8], and eventually increase the economic benefits of agricultural production. Consequently, detecting the purity of seed cultivars is necessary before sowing to avoid different cultivars of seeds being mixed during the production process, such as cultivation, harvesting, transportation, and storage procedures [6]. Morphology identification, physiochemistry analysis, and molecular identification are three types of conventional methods that may be used to identify sweet corn cultivars. These methods, including protein electrophoresis and DNA molecular markers, are time-consuming, costly, and destructive [9]. Thus, these methods may be used to detect a small group of sampling seeds. The complex sample preparation required for such detection methods also limits the possibilities of using these methods for online detection in the seed industry [10]. In view of these drawbacks, researchers have shown great interest in looking for rapid and non-destructive methods for seed cultivar detection. Hyperspectral imaging (HSI) is recognized to be a promising technique to detect seed cultivars by combining the advantages of computer vision and near-infrared spectroscopy (NIRS). It obtains spatial images of samples over a range of electromagnetic spectrums. The HSI data are generally described as a three-dimensional cube, which has two dimensions for pixel coordinates in the physical and spatial domain and one dimension for wavelengths. As previous research has reported, [PERSON] et al., (2012) applied an HSI system (380-1030 nm) for distinguishing varieties of maize seeds, where the highest accuracy at 98.89% was achieved by a support vector machine (SVM) algorithm [11]. [PERSON] et al., (2013) used an HSI system (1039-1612 nm) to identify rice seed cultivars, and the prediction rates of all the tested models were over 80%, and they also reported that the performances of the optimal wavelength-based were worse than the full-range models under the experimental data [12]. [PERSON] et al., (2015) studied the feasibility of classifying the varieties of waxy maize seeds using a customized HSI system (400-1000 nm), where they found that the spectra collected from the germ side generated a slightly higher accuracy than the spectra collected from the endosperm side [6]. [PERSON] et al., (2016) successfully utilized an HSI (400-1000 nm) to classify different varieties of maize seeds by combining spectral data and textural features [7].