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Abstract
Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.
Highlights
Okra (Abelmoschus esculentu (L.) Moench), known as a supervising versatile vegetable, has been widely cultivated all over the world
Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish the discrimination models based on the Discrimination models which could the hybrid okra seeds on theindex full of full spectrum, and the accuracy of theclassify classification recognition waswere used built as anbased evaluation spectrum
The classification results using the SVM model were superior to the PLS-DA classification results that are shown in Table 2, and the competitive adaptive reweighted sampling (CARS) algorithm had better discrimination results than successive projection algorithm (SPA) and CARS + SPA
Summary
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]. 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. Hybrid okra seeds have heterosis values that can rapidly increase productivity, improve the quality of okra as a food, and so on [7]
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