Year classification of high-oleic peanut seeds based on hyperspectral hybrid bands selection method.

Abstract

Seeds storage-year have a significant impact on high-oleic peanut seed vigor and quality. Therefore, it is essential to identify different storage-year seeds for planting, direct consumption, industrial processing, and marketing. In this study, hyperspectral images with 616 spectral bands (from visible light to near-infrared) were employed to classify different storage-year peanut seeds. To extract characteristic information for classification, we proposed a hybrid band selection (HBS) method based on the successive projection algorithm (SPA) by fusing the color-sensitive bands and moisture-sensitive bands. Then three classifiers, support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbors (KNN), were selected for storage-year classification. The experimental results demonstrated that the features extracted with the HBS method can obtain higher classification accuracy than other methods'. Specifically, the HBS-ELM model achieved the highest classification performance, with accuracy of 90.22%.

Plant phenotyping relevance

ハイパースペクトル画像から落花生種子の貯蔵年を推定するバンド選択法を開発・比較しており、種子の状態・品質の表現型抽出が研究の中心である。

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