Abstract：
This study explored the feasibility of employing hyperspectral imaging (HSI) technology to quantitatively assess the effect of silicon (Si) on lead (Pb) content in oilseed rape leaves. Aiming at the defects of hyperspectral data with high dimension and redundant information, this paper proposed two improved feature wavelength extraction algorithms, repetitive interval combination optimization (RICO) and interval combination optimization (ICO) combined with stepwise regression (ICO-SR). The entire oilseed rape leaves were taken as the region of interest (ROI) to extract the visible near-infrared hyperspectral data within the 400.89-1002.19 nm range. In data processing, Savitzky-Golay (SG) smoothing, detrending (DT), and multiple scatter correction (MSC) were utilized for spectral data preprocessing, while recursive feature elimination (RFE), iteratively variable subset optimization (IVSO), ICO, and the two enhanced algorithms were employed to identify characteristic wavelengths. Subsequently, based on the spectral data of preprocessing and feature extraction, partial least squares regression (PLSR) and support vector regression (SVR) methods were used to construct various Pb content prediction models in oilseed rape leaves, with a comparison and analysis of each model performance. The results indicated that the two improved algorithms were more efficient in extracting representative spectral information than conventional methods, and the performance of SVR models was better than PLSR models. Finally, to further improve the prediction accuracy and robustness of the SVR models, the whale optimization algorithm (WOA) was introduced to optimize their parameters. The findings demonstrated that the MSC-RICO-WOA-SVR model achieved the best comprehensive performance, with Rp2 of 0.9436, RMSEP of 0.0501 mg/kg, and RPD of 3.4651. The results further confirmed the great potential of HSI combined with feature extraction algorithms to evaluate the effectiveness of Si in alleviating Pb stress in oilseed rape and provided a theoretical basis for determining the appropriate amount of Si application to alleviate Pb pollution in oilseed rape.
摘要：
这项研究探讨了采用高光谱成像（HSI）技术定量评估硅（Si）对油菜叶片中铅（Pb）含量的影响的可行性。针对高光谱数据维数高、信息冗余的缺陷,本文提出了两种改进的特征波长提取算法,重复区间组合优化(RICO)和区间组合优化(ICO)结合逐步回归(ICO-SR)。将整个油菜籽油菜叶片作为感兴趣区域（ROI），以提取400.89-1002.19nm范围内的可见近红外高光谱数据。在数据处理中,Savitzky-Golay(SG)平滑，去趋势(DT),并利用多重散射校正(MSC)进行光谱数据预处理，而递归特征消除(RFE)，迭代可变子集优化(IVSO)，ICO,并采用两种增强算法来识别特征波长。随后，基于光谱数据的预处理和特征提取,采用偏最小二乘回归(PLSR)和支持向量回归(SVR)方法构建了油菜叶片Pb含量预测模型,并对每个模型的性能进行了比较和分析。结果表明,两种改进算法在提取有代表性的光谱信息方面比传统方法更有效,SVR模型的性能优于PLSR模型。最后,为了进一步提高SVR模型的预测精度和鲁棒性，引入鲸鱼优化算法(WOA)对其参数进行优化。结果表明，MSC-RICO-WOA-SVR模型取得了最佳的综合性能，Rp2为0.9436，RMSEP为0.0501mg/kg，RPD为3.4651。研究结果进一步证实了HSI结合特征提取算法在评价Si缓解油菜Pb胁迫效果方面的巨大潜力,为确定Si适宜施用量缓解油菜Pb污染提供了理论依据。
