Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model.

Abstract

Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67-100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60-100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.

Plant phenotyping relevance

イネ種子の純度識別を対象に、ハイパースペクトル画像とLASSOロジスティック回帰を組み合わせた識別手法を開発・評価しており、種子形質の取得・推定法が研究の中心である。

Code and data availability

The paper's hyperspectral rice seed data and LLRM analysis assets are not publicly deposited; the Data Availability Statement states data are available only upon request from the authors. No public URL for data or code is provided.

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