Biological Systems Engineering, Department of


Date of this Version

Fall 12-3-2010

Document Type



A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Agricultural and Biological Systems Engineering, Under the Supervision of Professor Viacheslav I. Adamchuk. Lincoln, Nebraska: December, 2010
Copyright 2010 Rajveer S Dhillon


One of the main goals of precision agriculture (PA) is to define spatial variability in soil properties within an agricultural field to make decisions that can maximize profitability and reduce negative environmental impact. Various soil sensor systems have been developed over the years to map soil properties on-the-go. In this study, an Integrated Soil Mapping System (ISMS) was developed to predict soil water content, soil organic matter, and soil mechanical resistance on-the-go using a capacitance moisture sensor, an optical sensor, and a load cell sensor respectively. These sensors were mounted on the ISMS for acquiring three different data layers at the same time. Each sensor was calibrated under laboratory conditions and the ISMS was also tested in fields. For example, volumetric soil water content estimated from the two-sided capacitance moisture sensor was compared with volumetric water content measured by the oven-drying method which produced R2 = 0.94 in laboratory conditions with a standard error of 0.017 cm3/cm3. Soil index calculated as the sum of individual soil reflectance measurements by the optical sensor in red (660 nm) and blue (480 nm) parts of the spectrum predicted soil organic matter with R2 = 0.73 and with standard error of 0.47 OM%. The load cell sensor was tested by applying different loads on the hitch for simulating field conditions and measuring value of known weights with an R squared = 0.99 and standard error of 0.032 kN. Then ISMS was tested in field conditions for mapping the three data layers simultaneously. High sampling density data collected by the ISMS was compared with data collected at the same time using conventional laboratory methods by developing the maps of soil properties and R2 of 0.74, 0.67 and 0.28 were obtained for the moisture, optical and load cell sensors, respectively when regressed with standard laboratory methods.