## 1 Introduction  
Maize (Zea mays), also known as corn, is a major source of food, forages, fuel, and industrial materials [1]. In recent years, the number of maize seed varieties has considerably increased because of the extensive application of seed hybrid technology. Moreover, in the process of cultivation, harvesting, storage, and transportation, each production procedure may lead to variety mixing, thereby complicating seed classification and decreasing crop yield. Research indicates that a 1% decrease in the purity of maize seeds can decrease maize yield to 9 kg per 667 m\({}^{2}\)[2]. Therefore, variety classification or identification before planting is important for maize seeds. Several traditional methods for maize seed classification have been developed over the past years and vary from each other, such as in morphology method, protein electrophoresis, and DNA molecular marker technology. Most of these methods require professional staff and specialized instruments, and they are often time consuming [3]. Although the traditional method is convenient and economic, its accuracy depends on the experience of the inspectors and is influenced by subjective errors. Therefore, the development of rapid, accurate, and nondestructive methods for classifying and identifying maize seeds is important in the seed industry. Accordingly, optical sensing techniques for the one-time measurement of quality parameters have been the focus of domestic and foreign research on the automated evaluation (classification/sorting) of maize seeds [4; 5]. Machine vision and conventional spectroscopy techniques are potential tools for the automatic quality evaluation of seeds [4; 6; 7; 8; 9]. However, whether machine vision or the conventional spectroscopy technique is used, some seeds may have similar appearances and differentiating them by image alone is difficult because seed cultivars have been increased. Researchers have studied hyperspectral imaging technology, which integrates the features of spectroscopy and machine vision in one system and provides spectral and spatial data simultaneously [10]. This technology has been widely evaluated by research groups in the quality assessment of agricultural products and foodstuffs. Hyperspectral imaging technology can provide the spectral and image features of measured objects, but the spectral features are the most commonly used in seed variety classification because of their easy calculation [11; 12; 13; 14]. The model based on spectral features will have the risk of classification accuracy deterioration if there are many seed varieties to be classified, or only a small amount of spectral information of seeds can be used because the need for rapid detection should be met. Therefore, researchers have attempted to extract more classification information from hyperspectral images to improve model performance. [PERSON] _et al._[15] used spectral and textural features (_i.e._, short-run emphasis, long-run emphasis, gray-level non-uniformity, run-length non-uniformity, and run percentage) extracted from six optimal wavelengths to develop the LS-SVM model for classifying the three variety of waxy maize and achieved classification accuracy of 88.89%. [PERSON] _et al._[16] reported a 98.2% prediction accuracy in classifying four varieties of maize seed using the combination of spectra, morphologic features (_i.e._, area, circularity, aspect ratio, roundness, and solidity) and texture features (_i.e._, energy, contrast, correlation, entropy, and standard deviations) extracted from the 19 optimal wavelengths for germ side. However, only part of the image features was used to classify a small number of varieties in their experiments. When a large number of varieties are to be classified, more classification features should be extracted from hyperspectral images. The extracted features usually contain irrelevant or even useless information because of the lack of effective knowledge to guide feature extraction, thus resulting in a dimension disaster for the classifier.