Abstract:
Degradation of citrus orchards production in the region may be caused by several factors including lower soil fertility, high salinity, and diseases. This study will identify the major issues that may affect the quantity and Quality of Citrus in the Sargodha district. Conventional techniques, including laboratory analysis used for soil & leaf physiochemical attributes estimation is time-consuming and costly. Modern scientific era demands a more effective methodology to estimate soil &leaf physiochemical attributes. Precision Agriculture, Remote Sensing & G.I.S. proved to be an effective remedy for this problem. Scientists worldwide are using Remote Sensing data with a variety of conventional and non-conventional methods to model and predict the different issues of kinnow crop. Classical Statistics have been widely used in research to model soil properties using remote sensing data. Growing G.I.S. knowledge has brought spatial regression modeling techniques to model and predict the citrus issues in any area. UAV monitoring and soil & leaf testing have brought further changes and addition to the subject a step ahead. The subject is in the exploratory phase and researchers are coming up with new methodologies and techniques to solve the citrus-related issue. This scientific research adds innovation to the subject by comparing various remote sensing techniques and two famous sensors Landsat-8 & UAV and the ground data for the mentioned purpose. This research focused on investigating soil &leaf chemical properties of the citrus crop using G.I.S. and Remote Sensing. To explore soil chemical properties, Classical statistics, Geostatistics and Spatial Interpolation (S.I.) were analyzed in this review. Inverse Distance Weighting (I.D.W.), Kriging Interpolation, Geospatial analysis were used to model and predict soil & leaf chemical properties of citrus crops. Soil Organic Matter (O.M.), pH, Nitrogen, Potassium and Phosphorus were tested with acceptable accuracy using variety of scientific techniques in the laboratory under a controlled atmosphere. Leaf mineral content also tested in the laboratory. UAV data and ground data were compared and evaluated for predicting the citrus' soil & leaf chemical properties (Kinnow). Using classical statistics, M.L.R. for spatial data may not be realistic since it does not consider spatial variability, limitations in classical statistical models were successfully overcome using Geostatistics & spatial regression. UAV data and better resolution were used to identify nutrient deficiency both in the leaf and soil of the study area. UAV monitoring along with soil & leaf testing of the area gave better results. For SI, no technique was found to be best; rather S.I. accuracy depends on data spread and magnitude.
