Dietary exposure to a variety of heavy metals, including Ni, Cd, Cr, Pb, Zn, and Hg, has been identified as a danger to human health through fruits and vegetables, contamination of heavy metals is known as a grave risk to our climate.
The study aims to develop empirical models to predict the concentration of heavy metals (Ni, Cd, Cr, Pb, Zn, and Hg) in the leaves of Citrus and Mango crops. The study was carried out in an observation site in Giza governorate that is cultivated by varied herbaceous and tree cover crops. This study area is suffering from severe pollution caused by near industrial district. The sample collected from deferent zones that are divided to six spatial zones and coded by from zone (2, 3, 4, 5, and 6). The distance between each Zone 10 Km that extends from the north to south and covers 60% from the Agriculture area in the Giza governorate. The main inputs of the generated models were spectroscopic remotely sensed data and laboratory analytical measurements of heavy metals in crop leaves. ASD (Analytical Spectral Devices) field spectro-radiometer was used to calculate hyper-spectral vegetation indices.
Modeled heavy metal concentrations were tested against laboratory analysis through two common statistical tests; the Correlation of determination (R2) and Root Mean square (RMSE) error between predicted modeled heavy metals. Results shown the correlation coefficient of the generated models, red and near-infrared spectral bands demonstrated high precision and sufficiency for mango and citrus leaves to predict heavy metals. The models produced refer to specific regions with the same conditions.
The overall results imply that hyper-spectral vegetation indices could be correlated with heavy metal content, while heavy metal content in plants may be influenced by many others.
Remote sensing spectroscopy is a possible and promising technology to track the environmental pressures on agricultural vegetation. Additional ground remote sensing experiments are needed to assess the possibility of hyper-spectral reflectance spectroscopy in monitoring the stress of different types of metals on various plants.