Analysis of true-color images from unmanned aerial vehicle to assess salinity stress on date palm

Soils in arid countries predominantly suffer from salinity and drought related to environmental problems that can lead to crop stress and low productivity. In this study, true-color aerial images from an unmanned aerial vehicle were used to assess the effect of soil and water salinity on date palm growth. Random soil samples (n = 75) and irrigation water samples were collected from five sites and chemically analyzed. The custom algorithms were developed in ENVI^® and MATLAB.^® Green leaf index (GLI) was implemented to determine crop canopy attributes. Two segmentation methods namely between-class variance and foreground pixels were used to recognize the vegetation cover from other image pixels. The image analysis demonstrated that the mean value of GLI increased as the salinity levels decreased, R = 0.96 and 0.92 for soil electrical conductivity (EC) and water EC, respectively. The percentage of area covered with vegetation was correlated to soil EC and water EC with about 70% accuracy. On the other hand, the percentage of area covered with palm trees only was used accurately to evaluate the soil EC by R² = 0.89 and the water EC by R² = 0.86. The findings of this research can set foundations for the development of aerial color imaging on salinity stressed date palm monitoring, providing useful information for decision makers on salinity management.