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Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Jan 15; 245:118917.SA

Abstract

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.

Authors+Show Affiliations

College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China.College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China.College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China. Electronic address: yrzhao@nwsuaf.edu.cn.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32949945

Citation

Yu, Keqiang, et al. "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, vol. 245, 2021, p. 118917.
Yu K, Fang S, Zhao Y. Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods. Spectrochim Acta A Mol Biomol Spectrosc. 2021;245:118917.
Yu, K., Fang, S., & Zhao, Y. (2021). 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, 245, 118917. https://doi.org/10.1016/j.saa.2020.118917
Yu K, Fang S, Zhao Y. Heavy Metal Hg Stress Detection in Tobacco Plant Using Hyperspectral Sensing and Data-driven Machine Learning Methods. Spectrochim Acta A Mol Biomol Spectrosc. 2021 Jan 15;245:118917. PubMed PMID: 32949945.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods. AU - Yu,Keqiang, AU - Fang,Shiyan, AU - Zhao,Yanru, Y1 - 2020/09/06/ PY - 2020/05/09/received PY - 2020/08/16/revised PY - 2020/09/01/accepted PY - 2020/9/20/pubmed PY - 2021/5/15/medline PY - 2020/9/19/entrez KW - Canopy KW - Heavy metal Hg stress KW - Machine learning methods KW - Proximal hyperspectral imaging KW - Tobacco plant SP - 118917 EP - 118917 JF - Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy JO - Spectrochim Acta A Mol Biomol Spectrosc VL - 245 N2 - 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. SN - 1873-3557 UR - https://www.unboundmedicine.com/medline/citation/32949945/Heavy_metal_Hg_stress_detection_in_tobacco_plant_using_hyperspectral_sensing_and_data_driven_machine_learning_methods_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1386-1425(20)30896-9 DB - PRIME DP - Unbound Medicine ER -