Development of IoT-Based Precision Irrigation System for Sustainable Water Use

Thesis Abstract

Water scarcity and inefficient usage of irrigation resources pose significant challenges to sustainable agriculture in arid and semi-arid regions. The intensified demand for water due to population growth and climate variability underscores the need for innovative solutions to optimize water use in agriculture. This study aims to develop an Internet of Things (IoT)-based precision irrigation system that enhances water efficiency and supports sustainable irrigation practices. The specific objectives include designing an IoT-enabled sensor network for real-time soil moisture monitoring, developing a decision-support system for automated irrigation control, and evaluating the system's performance under different crop and soil conditions. The research adopts a mixed-methods approach, integrating quantitative experimental design with qualitative usability evaluation. The study population comprises smallholder farmers and agricultural extension officers in a semi-arid region with diverse soil types and crop varieties. A sample size of 120 farmers was determined using Cochran's formula, and participants were selected through stratified random sampling to ensure representation across different farm sizes and crop types. Data collection instruments include soil moisture sensors, weather stations, smartphone-based interfaces, and structured questionnaires for user feedback. The system prototype was developed utilizing Arduino microcontrollers, LoRaWAN communication modules, and cloud-based data management platforms. Data analysis involves multiple techniques, including descriptive statistics to summarize sensor data and user responses, regression analysis to assess factors influencing system adoption, and ANOVA to compare water usage before and after system implementation. The system's technical performance was evaluated through field experiments measuring irrigation water volumes, crop water stress indices, and yield parameters over a growing season. Qualitative data from focus group discussions and usability tests were analyzed using thematic analysis to identify barriers and facilitators to system usability and acceptance. The anticipated findings suggest that the IoT-enabled precision irrigation system significantly reduces water consumption by an average of 30% compared to conventional practices without compromising crop yields. The system's real-time soil moisture monitoring and automated control logic are expected to improve irrigation scheduling accuracy and resource use efficiency. Additionally, the study is projected to reveal key behavioral and infrastructural factors affecting system adoption, including technological literacy, network connectivity, and perceived cost benefits. This research contributes to the body of knowledge by demonstrating the integration of IoT technologies into agricultural water management systems, offering a scalable model for resource-efficient irrigation. It advances understanding of user-centered design considerations in deploying smart irrigation solutions within smallholder farming contexts. The study's findings will inform policymakers and extension services on harnessing digital technologies to promote sustainable water use practices in agriculture. The main conclusions indicate that IoT-based precision irrigation is a viable strategy for resource conservation and yield enhancement in water-scarce regions. It recommends fostering capacity-building programs to enhance digital literacy among farmers, improving rural connectivity infrastructure, and developing affordable sensor and control systems tailored to smallholder needs. Future research should explore long-term impacts, economic viability, and integration with other agro-advisory services, thus contributing to the broader goal of sustainable agricultural development.