Yang Bing, Liu Xuyang, Zhang Dongfang, Fan Xiaofei, Peng Bo, Zhang Jun
College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China.
College of Economics and Management, Hebei Agricultural University, Baoding, China.
Front Plant Sci. 2025 Jun 3;16:1584269. doi: 10.3389/fpls.2025.1584269. eCollection 2025.
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.
茄子种子活力是其发芽率和幼苗生长质量的关键指标。为了满足高效无损评估方法的需求，本研究探索了利用高光谱成像结合先进的特征选择和分类算法来评估茄子种子活力。采用高光谱成像技术从茄子种子中采集光谱数据，覆盖波长范围为395.24 - 1008.20 nm的360个波段。种子经过微波加热和恒温水浴老化处理。数据预处理涉及三种技术：多元散射校正（MSC）、Savitzky - Golay（SG）平滑和标准正态变量（SNV）变换。提出了一种增强信息获取优化（EIAO）算法进行特征选择，成功识别出23个关键波长的最小集合。使用极限学习机（ELM）、随机森林（RF）和支持向量机（SVM）建立种子活力分类模型。ELM的最佳分类准确率为90.0%，RF为91.45%，SVM为90.5%。MSC - EIAO - RF模型表现最佳，准确率达到91.45%，比MSC - IAO模型（82.41%）高9.04%。在四个UCI数据集上的验证进一步证实了EIAO算法优于传统特征选择方法。这些结果验证了高光谱成像结合EIAO用于无损种子活力检测的稳健性和通用性，为种子质量评估提供了一种智能高效的解决方案。
