Abstract
Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350–2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.
Highlights
In-field spectroscopy provides a time and cost-efficient and accurate way to monitor plant stress [1,2,3]
We examined the suitability of in-field hyperspectral data in the estimation of heavy metal contents (Cu, Zn, Pb, Cr, and Cd) in vine leaves
(ii) Significant correlations are found between the heavy metal contents and the grapevine’s foliar spectral response, especially in VIS and RDE regions. (iii) From the reflectance data, 32 spectral indices were formulated using two or more bands
Summary
In-field spectroscopy provides a time and cost-efficient and accurate way to monitor plant stress [1,2,3]. Several studies have made specific use of the application of crop spectral characteristics through in-field spectroscopy data and multivariate statistical analysis to promote the prediction of heavy metal content in plant samples. Identifying optimal wavelengths to monitor any parameter—e.g., heavy metal concentrations—is an important step in applying these data [41] In this regard, the usage of multivariate statistical techniques such as the PLS method [14,40,42,43], multiple linear regression (MLR) [41,44,45], and support vector machines (SVM) [12,40,46] can help with feature selection, data reduction, and modelling the existing relationships between hyperspectral data and plant characteristics. As a common grapevine variety in Iran) by using full range in-situ spectroscopy (350-2500 nm), (ii) evaluating the potential of in-field spectroscopy for estimating heavy metals (Cu, Zn, Pb, Cr, and Cd) concentrations in grapevine foliage, (iii) investigating two types of hyperspectral data (wavelengths vs. spectral indices) and identifying the most appropriate features to estimate each studied metal in grapevine foliage, and (iv) comparing the performance of SVM and MLR algorithms in modeling the relationships between the foliar spectral response and heavy metal concentrations
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