Assessment of Spectroscopic and Morphological Properties of some Fruit Crops under the Influence of Pollution with Heavy Metals Using Remote Sensing Techniques

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

Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article)

Pre-harvest prediction of a crop yield may prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. Remote sensing has numerous returns in the area of crop monitoring and yield prediction which are closely related to differences in soil, climate, and any biophysical and biochemical changes. Different remote techniques could be used for crop monitoring and yield prediction including multi and hyper spectral data, radar and lidar imagery. This study reviews the potentialities, advantages and disadvantages of each technique and the applicability of these techniques under different agricultural conditions. It also shows the different methods in which these techniques could be used efficiently. In addition, the study expects future scenarios of remote sensing applications in vegetation monitoring and the ways to overcome any obstacles that may face this work. It was found that using satellite data with high spatial resolution are still the most powerful method to be used for crop monitoring and to monitor crop parameters. Assessment of crop spectroscopic parameters through field or laboratory devices could be used to identify and quantify many crop biochemical and biophysical parameters. They could be also used as early indicators of plant infections; however, these techniques are not efficient for crop monitoring over large areas