Spectral Characteristics for Estimation Heavy Metals Accumulation in Wheat Plants and Grain

Citation

Amer M, Tyler A, Fouda T, Hunter P, Elmetwalli AMH, Wilson C & Vallejo-Marín M (2017) Spectral Characteristics for Estimation Heavy Metals Accumulation in Wheat Plants and Grain. Scientific Papers Series: Management, Economic Engineering and Rural Development, 17 (3), pp. 47-55. http://managementjournal.usamv.ro/index.php/scientific-papers/1451-spectral-characteristics-for-estimation-heavy-metals-accumulation-in-wheat-plants-and-grain-1451

Abstract
Plants would the start with step of a metal's pathway starting with the dirt on heterotrophic creatures for example, such that animals and humans, thus the substance from claiming metallic follow components for eatable parts of a plant representable accessible load of these metals that might enter those natural way of life through plants. Around metal elements, Cu and Zn would micro nutrients as they are essential in trace concentrations for physiological processes in plants. Furthermore consequently would a critical part from the soil–plant–food continuum. Therefor this study aimed to analysing the performance of multivariate hyperspectral vegetation indices of wheat (Triticum aestivum L.) in estimating the accumulation of these elements in plant dry mutter and the final product of Egyptian wheat crop irrigated with high concentrations of Zn and Cu. We applied five concentrations for each element (0.05, 20, 40, 100, and 150 ppm of Zn) and (0.02, 8, 10, 12, and 15 ppm of Cu) to a controlled greenhouse experiment to examine the effect of these concentrations on plant spectral characteristics and study the possibility of using spectroradiometry measurements for identifying the grain content of these metals. The results demonstrated that The hyperspectral vegetation indices had a potential for monitoring Zn concentration in the plant dry matter. NPCI and PSSR had a highest correlation with Cu phytoaccumulation into the grains with highest significant level (P-Value < 0.01) and (r) values (-0.39, -0.42).

Keywords
heavy metals; remote sensing; vegetation indices; detecting stress

Journal
Scientific Papers Series: Management, Economic Engineering and Rural Development: Volume 17, Issue 3

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