Geospatial analysis of Citrus orchard soil and leaf nutrient concentration and its relationship with high resolution drone imagery and yield

Abstract

It is obvious that the citrus sickness has arisen as a likely genuine danger to citrus creation in all the citrus developing nations of the world including Pakistan. Precision agriculture (PA) is effective remedy to overcome the challenge. This study will identify the major issues which may affect the quantity and quality of citrus in the Sargodha district. Determination of soil physio-chemical attributes is first phase of PA. Conventional techniques including laboratory analysis used for soil & leaf physio-chemical attributes estimation are time consuming and costly. Modern scientific era demands a more effective methodology to estimate soil &leaf physio-chemical attributes. Precision Agriculture, Remote Sensing & GIS proved to be an effective remedy for this problem. Scientist all over the world are using Remote Sensing data with 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 knowledge of GIS have brought spatial regression modelling techniques to model and predict the citrus issues in any area. Moving a step ahead UAV monitoring and soil & leaf testing have brought further changes and addition to subject. The subject is in exploratory phase and researchers are coming up with new methodologies and techniques to solve the citrus related issue. This scientific research adds an innovation to subject by comparing not only various remote sensing techniques but also two famous sensors Landsat-8 & UAV along with the ground data for mentioned purpose. Focus of this research was to investigate soil &leaf chemical properties of the citrus crop using GIS and Remote Sensing. To explore soil chemical properties, Classical statistics, Geo statistics and Spatial Interpolation (SI) were analyzed in this review. Inverse Distance Weighting (IDW), Kriging Interpolation, Geospatial analysis were used to model and predict soil & leaf chemical properties of citrus crop. Soil Organic Matter (OM), pH, Nitrogen, Potassium and phosphorus were tested with acceptable accuracy using variety of scientific techniques in laboratory under controlled atmosphere. Leaf mineral content also tested in laboratory. UAV data along with ground data were compared and evaluated for predicting soil & leaf chemical properties of the citrus (Kinnow). Using classical statistics, MLR for spatial data may not be realistic since it does not consider spatial variability, limitations in classical statistical models were successfully overcome using Geo statistics & spatial regression. UAV data along with better resolution was used to identify nutrient deficiency both in 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 SI accuracy depends on data spread and magnitude.