Development of IoT-based Soil Moisture Monitoring System for Precision Irrigation

Thesis Abstract

In the face of increasing water scarcity and the imperative for sustainable agricultural practices, efficient irrigation management has emerged as a critical challenge confronting modern agriculture. Conventional irrigation techniques often result in inefficient water use, leading to crop stress, reduced yields, and environmental degradation. This study aims to develop an Internet of Things (IoT)-based soil moisture monitoring system designed to enhance precision irrigation practices, thereby optimizing water use and improving crop productivity. The research objectives include designing a low-cost, scalable sensor network for real-time soil moisture data acquisition, implementing a data transmission framework utilizing wireless communication protocols, and developing a decision-support system to inform irrigation scheduling based on sensor insights. Employing a mixed-methods approach, the study adopted a quasi-experimental research design within a controlled agricultural environment encompassing twenty-five experimental plots cultivated with maize. The population comprised soil sensors, field data collectors, and local farmers, with a purposive sample of twenty agricultural technicians and fifty farmers participating in system deployment, calibration, and validation. Data collection instruments included sensor hardware for measuring volumetric soil moisture content, a custom-built data acquisition interface, and structured questionnaires to assess user acceptance and system usability. Data analysis was conducted using descriptive statistics for system performance metrics, regression analysis to determine the relationship between soil moisture levels and crop yield, and thematic analysis for qualitative feedback from stakeholders. Analytical models involved the application of multiple linear regression to evaluate the predictive capacity of soil moisture data on irrigation needs and the implementation of ANOVA to compare water use efficiency between traditional and IoT-guided irrigation practices. Expected findings suggest that the IoT-based soil moisture monitoring system will provide highly accurate, real-time data, leading to significant improvements in water use efficiency—anticipated reductions of at least 30% compared to conventional methods—and an increase in maize yield by approximately 15%. The system’s predictive analytics are projected to facilitate precise irrigation scheduling, reducing water wastage and enhancing crop health. Additionally, the study aims to identify key user acceptance factors influencing technology adoption among farmers, framed within the Technology Acceptance Model (TAM), and supported by the Unified Theory of Acceptance and Use of Technology (UTAUT). The anticipated results will contribute novel insights into integrating IoT solutions within smallholder and commercial farming contexts, advancing the application of precision agriculture technologies. This research advances knowledge by presenting an integrated, scalable IoT architecture tailored for soil moisture monitoring in semi-arid agricultural settings, addressing existing gaps related to system affordability, usability, and ecological adaptability. The study’s comprehensive validation and stakeholder engagement strategies offer a practical blueprint for widespread deployment of sensor-based irrigation management systems. The main conclusions underscore the potential for IoT-enabled solutions to transform irrigation practices, improve resource sustainability, and bolster food security. It is recommended that future research explore the integration of additional environmental sensors, such as temperature and humidity, and expand the system’s deployment across diverse crop types and climatic zones to enhance generalizability. Furthermore, policy frameworks should be developed to incentivize adoption among smallholder farmers and facilitate infrastructure support for remote monitoring and system maintenance. Overall, this thesis demonstrates that leveraging IoT technology can significantly contribute to sustainable, data-driven agriculture, supporting both economic and environmental objectives.