Identifying cadmium and lead co-accumulation from living rice blade spectrum

Neither cadmium (Cd) nor lead (Pb) is necessary for crop growth, but they both can accumulate in soil and crop tissues, resulting in land degradation and crop reduction. Few researchers have explored how to detect Cd–Pb co-accumulation in leaves using proximal sensing techniques, especially by low-c...

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Vydáno v:Environmental pollution (1987) Ročník 338; s. 122618
Hlavní autoři: Zhang, Shuangyin, Fei, Teng, Chen, Yiyun, Yang, Jiaxin, Qu, Ran, Xu, Jian, Xiao, Xiao, Cheng, Xuejun, Hu, Zhongzheng, Zheng, Xuedong, Zhao, Dengzhong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.12.2023
Témata:
cadmium
canopy
Cross-contamination
Foliar position
greenhouse experimentation
Heavy metal pollution
Hyperspectral
hyperspectral imagery
land degradation
lead
leaves
pollution
prediction
rice
soil
support vector machines
Cross-contamination
Foliar position
Heavy metal pollution
Hyperspectral
ISSN:0269-7491, 1873-6424, 1873-6424
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Shrnutí:Neither cadmium (Cd) nor lead (Pb) is necessary for crop growth, but they both can accumulate in soil and crop tissues, resulting in land degradation and crop reduction. Few researchers have explored how to detect Cd–Pb co-accumulation in leaves using proximal sensing techniques, especially by low-cost, easy-to-use leaf clips that capture hyperspectral reflections at suitable foliar positions. In this study, a hyperspectral imager was employed to collect images of the rice canopy from a designed greenhouse experiment that included 16 pretreatments of Cd–Pb co-accumulation, followed by spectral extractions from 3 foliar positions: the blade root, the middle of the leaf, and the leaf apex. A support vector machine with leave-one-out cross-validation was performed to diagnose the contaminative levels based on the feature wavelengths selected by an improved successive projection algorithm. Partial least squares regression was used to predict Cd–Pb concentrations in rice blades. The results indicated that diagnostic accuracies were varied using spectra of different foliar positions. The blade root and leaf apex of rice blades were the optimal foliar position for detecting Cd and Pb contamination, respectively. At the optimal foliar positions, diagnostic accuracies exceeded 0.80 for distinguishing whether the rice is subject to Cd–Pb contamination. The Cd prediction performed ‘very good’ with a residual prediction deviation (RPD) of 2.21, a R2 of 0.79, and a root mean square error (RMSE)of 6.14, while that of Pb was 1.62, 0.61, and 186.54. Important wavelengths were identified at 659–694 nm and 667–694 nm to detect Cd and Pb contamination. In summary, our results verified the feasibility and clarified the optimal foliar positions of rice blades to detect Cd–Pb contamination. The wavelengths selecting have the great potential in the design of future leaf clips, and the optimal foliar position can provide suggestions to improve diagnostic performances in field applications. [Display omitted] •Hyperspectral images of rice canopy under Cd–Pb cross-contamination were measured.•Spectra of three foliar positions were extracted to detect Cd–Pb contamination.•The blade root and leaf apex of blades were the optimal detecting foliar position.•659–694 nm and 667–694 nm have the potential in the design of future leaf clips.
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content type line 23
ISSN:0269-7491
1873-6424
1873-6424
DOI:10.1016/j.envpol.2023.122618