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Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy ( IF 4.4 ) Pub Date : 2020-09-06 , DOI: 10.1016/j.saa.2020.118917
Keqiang Yu 1 , Shiyan Fang 1 , Yanru Zhao 1
Affiliation  

Accurate detection of heavy metal stress on the growth status of plants is of great concern for agricultural production and management, food security, and ecological environment. A proximal hyperspectral imaging (HSI) system covered the visible/near-infrared (Vis/NIR) region of 400–1000 nm coupled with machine learning methods were employed to discriminate the tobacco plants stressed by different concentration of heavy metal Hg. After acquiring hyperspectral images of tobacco plants stressed by heavy metal Hg with concentration solutions of 0 mg·L−1 (non-stressed groups), 1, 3, and 5 mg·L−1 (3 stressed groups), regions of interest (ROIs) of canopy in tobacco plants were identified for spectra processing. Meanwhile, tobacco plant's appearance and microstructure of mesophyll tissue in tobacco leaves were analyzed. After that, clustering effects of the non-stressed and stressed groups were revealed by score plots and score images calculated by principal component analysis (PCA). Then, loadings of PCA and competitive adaptive reweighted sampling (CARS) algorithm were employed to pick effective wavelengths (EWs) for discriminating non-stressed and stressed samples. Partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were utilized to estimate the stressed tobacco plants status with different concentrations Hg solutions. The performances of those models were evaluated using confusion matrixes (CMes) and receiver operating characteristics (ROC) curves. Results demonstrated that PLS-DA models failed to offer relatively good result, and this algorithm was abandoned to classify the stressed and non-stressed groups of tobacco plants. Compared to LS-SVM model based on full spectra (FS-LS-SVM), the LS-SVM model established EWs selected by CARS (CARS-LS-SVM) carried 13 variables provided an accuracy of 100%, which was promising to achieve the qualitative discrimination of the non-stressed and stressed tobacco plants. Meanwhile, for revealing the discrepancy between 3 stressed groups of tobacco plants, the other FS-LS-SVM, PCA-LS-SVM, and CARS-LS-SVM models were setup and offered relatively low accuracies of 55.56%, 51.11% and 66.67%, respectively. Performance of those 3 LS-SVM discriminative models was also poorly performing to differentiate 3 stressed groups of tobacco plants, which might be caused by low concentration of heavy metal and similar canopy (especially in fresh leaves) of plant. The achievements of the research indicated that HSI coupled with machine learning methods had a powerful potential to discriminate tobacco plant stressed by heavy metal Hg.



中文翻译:

使用高光谱传感和数据驱动的机器学习方法检测烟草厂中的重金属汞应力。

准确检测重金属对植物生长状况的压力,对于农业生产和经营,食品安全以及生态环境都至关重要。近端高光谱成像(HSI)系统覆盖了400-1000 nm的可见/近红外(Vis / NIR)区域,并结合机器学习方法来区分受不同浓度重金属Hg胁迫的烟草植株。在获取重金属汞胁迫的烟草植物的高光谱图像后,浓度为0 mg·L -1(非胁迫组),1、3和5 mg·L -1(3个压力组),确定了烟草植物冠层的感兴趣区域(ROI)以进行光谱处理。同时,分析了烟草植株的外观和叶片中叶肉组织的微结构。之后,通过得分图和通过主成分分析(PCA)计算出的得分图像揭示了非压力组和压力组的聚类效果。然后,采用PCA的加载量和竞争性自适应加权加权采样(CARS)算法来选择有效波长(EWs),以区分无应力和有应力的样品。利用偏最小二乘判别分析(PLS-DA)和最小二乘支持向量机(LS-SVM)来估算不同浓度的Hg溶液对烟草的胁迫状态。使用混淆矩阵(CMes)和接收器工作特性(ROC)曲线评估了这些模型的性能。结果表明,PLS-DA模型无法提供相对较好的结果,因此放弃了该算法来对有压力和无压力的烟草植物进行分类。与基于全光谱的LS-SVM模型(FS-LS-SVM)相比,由CARS选择的EW所建立的LS-SVM模型(CARS-LS-SVM)带有13个变量,提供了100%的精度,有望实现对无压力和有压力烟草植物的定性歧视。同时,为了揭示烟草植物的3个压力组之间的差异,建立了其他FS-LS-SVM,PCA-LS-SVM和CARS-LS-SVM模型,并提供了相对较低的准确度,分别为55.56%,51.11%和66.67。 %, 分别。这3种LS-SVM判别模型的性能在区分3种受压烟草植物方面也表现不佳,这可能是由于植物中低浓度的重金属和类似的冠层(尤其是鲜叶)所致。研究成果表明,HSI与机器学习方法相结合,具有区分重金属汞胁迫的烟草植物的强大潜力。

更新日期:2020-09-16
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