Abstract
The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874–1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry.
Highlights
As staple food of thousands of millions of people, wheat (Triticum aestivum L.)’s production amounted to 772 million tonnes totally each year [1], while its supply was just 179.26 g per day per capita [2] in the world
The goal of classifying five varieties of wheat seeds was implemented and while using hyperspectral imaging technology combined with chemometric methods
During the completed while using hyperspectral imaging technology combined with chemometric methods
Summary
As staple food of thousands of millions of people, wheat (Triticum aestivum L.)’s production amounted to 772 million tonnes totally each year [1], while its supply was just 179.26 g per day per capita [2] in the world. With social economy and people’s living standard growing, there are more demanding requirements for wheat grain’s quality and yield. Wheat seeds with high quality play an essential role in the improvement of wheat’ yield, in which one of the most significant factors is the varieties purity of wheat seeds. Low purity seeds will lead to huge economic loss in terms of breeding, planting, and commodity’s quality for farmers and consumers. With the continual circulation of the large-scale seeds in modern seed industry, it is likely to result in an accidental mixing between different varieties of wheat seeds during transportation, storage, and production, which will inevitably decrease wheat quality and yield. It is considerable to develop a rapid approach to identify and classify different varieties of wheat seeds
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