# Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
## 1 Introduction  
Okra (_Abelmoschus esculentu_ (L.) _Moench_), known as a supervising versatile vegetable, has been widely cultivated all over the world. It is a powerhouse of various nutrients, such as protein, cellulose, unsaturated fatty acids, and minerals, such as iron, calcium, manganese, potassium, zinc, and so on [1]. Additionally, it is low in calories and fat free [2]. It has been discovered that okra seeds are rich in flavonoids and polyphenols, all of which have strong anti-oxidative, anti-fatigue, and anti-cancer abilities [3; 4; 5; 6]. Screening and identification of seeds has always been an important part of the agricultural breeding process. Breeding specialists typically cross-fertilize different pure lineages of the desired trait to produce offspring heterosis. At present, many studies have focused on the breeding of okra, which includes hybridization breeding [7; 8; 9]. Hybrid okra seeds have heterosis values that can rapidly increase productivity, improve the quality of okra as a food, and so on [7]. However, the process of obtaining hybrid okra seeds is time-consuming and laborious. Breeding experts often have to plant hybrids to a certain stage in order to screen the seeds [8; 9]. They used plant characteristics, such as plant height, leaf width, and fruit length to select the optimal hybrid offspring. Spectroscopic and spectral imaging techniques provide comprehensive structural information on the components and properties of samples at the molecular level [10]. Nowadays, near-infrared hyperspectral technology has been widely used in food detection and the identification of varieties [11; 12; 13; 14; 15]. Near-infrared hyperspectral imaging is a fast nondestructive detection technology combining machine vision and visible/near-infrared spectroscopy. With the help of near-infrared hyperspectral imaging, the spatial and spectral information of the samples can be contained simultaneously. Hundreds of contiguous wavebands for each spatial position of the sample make up the near-infrared hyperspectral images [16]. The spatial and spectral information that represents the external and internal information of the sample is provided in hypercube form and it is possible to obtain multiple sample spectra in a single scan [17; 18]. So, it is very effective at detecting the seeds of hybrid okra by near-infrared hyperspectral imaging. Because different kinds of seeds contain different material information, seeds can be classified by near-infrared hyperspectral imaging combined with chemometrics. [PERSON] et al. (2017) identified the varieties of maize seeds using hyperspectral imaging and chemometrics [19]. They also studied the influence of calibration sample size on classification accuracy and obtained satisfactory results while using the radial basis function neural networks (RBFNN) model with a calibration accuracy of 93.85% and a prediction accuracy of 91.00%. Apart from pure seed classification, there are some studies that are focused on the spectral changes of seeds with different treatments. [PERSON] et al. (2017) used near-infrared hyperspectral imaging technology and multidimensional data processing and analysis methods to distinguish transgenic maize seeds, and they managed to achieve a classification accuracy of up to 99.43% with the partial least squares discriminant analysis [20]. [PERSON] et al. (2016) used near-infrared hyperspectral imaging to distinguish corn seeds of different years. They applied model updating to update the least squares support vector machines (LSSVM) model and the classification accuracy reached 94.40%, which was 10.30% higher than that of non-updated models [13]. [PERSON] et al. (2016) used near-infrared hyperspectral imaging of a single tomato seed combined with multidimensional data processing methods to analyze the quality of tomato [21]. [PERSON] et al. (2013) used near-infrared hyperspectral imaging to distinguish jatropha seeds from different geographical environments, with an identification rate of 93.75% [22].