Optimized wavelength selection for eggplant seed vitality classification using information acquisition techniques.

Abstract

Eggplant seed vigor is a crucial indicator of its germination rate and seedling growth quality. In response to the need for efficient and nondestructive assessment methods, this study explores the use of hyperspectral imaging combined with advanced feature selection and classification algorithms to evaluate eggplant seed viability. Hyperspectral imaging was employed to collect spectral data from eggplant seeds, covering 360 bands within a wavelength range of 395.24-1008.20 nm. The seeds underwent microwave heating and constant-temperature water bath aging treatments. Data preprocessing involved three techniques: Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG) smoothing, and Standard Normal Variate (SNV) transformation. An Enhanced Information Acquisition Optimization (EIAO) algorithm was proposed for feature selection, which successfully identified a minimal set of 23 key wavelengths. Seed vigor classification models were developed using Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM).The optimal classification accuracies achieved were 90.0% for ELM, 91.45% for RF, and 90.5% for SVM. The MSC-EIAO-RF model demonstrated the best performance, achieving an accuracy of 91.45%, which is 9.04% higher than the MSC-IAO model (82.41%).Validation on four UCI datasets further confirmed the EIAO algorithm's superiority over conventional feature selection methods. These results verify the robustness and generalizability of hyperspectral imaging combined with EIAO for nondestructive seed viability detection, offering an intelligent and efficient solution for seed quality assessment.

Plant phenotyping relevance

ナス種子の活力・生存性を非破壊評価するためのハイパースペクトル画像法、波長選択アルゴリズム、分類モデルを中心に開発・検証しており、植物形質測定法に該当する。

Code and data availability

The supplied blocks describe hyperspectral imaging of eggplant seeds and the EIAO wavelength-selection algorithm, but contain no public dataset, image, code, or model deposit. No data availability statement text or repository/URL appears in the supplied material; only the article DOI is present.

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