Biological Systems Engineering, Department of
First Advisor
Abia Katimbo
Committee Members
Derek Heeren, Xin Qiao, Hongzhi Guo, Yufeng Ge
Date of this Version
11-2024
Document Type
Thesis
Citation
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 Abia Katimbo
Lincoln, Nebraska, November 2024
The first chapter of this thesis is published in Sensors Journal, available at https://www.mdpi.com/1424-8220/24/23/7480
Abstract
Efficient water management in agriculture is critical, as irrigation accounts for 70% of global freshwater withdrawals. While precision agriculture technologies offer potential solutions, integrating diverse data streams for real-time irrigation scheduling remains challenging, particularly in combining plant and soil-based stress indicators. This research addresses these challenges through three interconnected studies.
A systematic review first examined the current state and future potential of IoT-based automated irrigation management systems, analyzing how these technologies can enhance agricultural water use efficiency and crop productivity. The review identified critical gaps in data integration and real-time processing while highlighting opportunities for combining multiple stress indices for improved irrigation management.
Building on these findings, the Crop2Cloud platform was developed and implemented at the West Central Research, Extension and Education Center in North Platte, Nebraska. The platform was developed during the 2023 growing season and deployed for real-time irrigation management throughout the 2024 season, where it actively guided irrigation decisions through automated recommendations. The system integrated data from soil moisture sensors at multiple depths, infrared radiometers, and weather stations through a comprehensive IoT and cloud computing infrastructure. This enabled real-time computation and visualization of Crop Water Stress Index (CWSI), Soil Water Stress Index (SWSI), and crop evapotranspiration (ETc) through an interactive dashboard.
The third study focused on assessing the effectiveness of different irrigation scheduling methods implemented through the Crop2Cloud platform. Treatments included CWSI-based, SWSI-based, combined index approaches, and ET-based scheduling. The platform demonstrated the technical feasibility of integrating multiple stress indices for irrigation scheduling while identifying practical challenges such as sensor reliability and power management issues during periods of low solar radiation.
This research contributes to the advancement of precision irrigation by documenting the development and implementation of a data-driven irrigation management system. The findings provide insights into the challenges and opportunities of integrating multiple stress indices for irrigation scheduling, while establishing a foundation for future research in automated irrigation technology.
Comments
Copyright 2024, Bryan Nsoh. Used by permission