Design and evaluation of an automated irrigation system using soil moisture sensors

Thesis Abstract

Increasing water efficiency in agricultural practices is a critical response to rising water scarcity and the need for sustainable resource management. This study addresses the persistent challenge of optimizing irrigation practices by developing and evaluating an automated irrigation system that utilizes soil moisture sensors for real-time water management. The primary aim is to design a cost-effective, reliable automation framework that enhances water use efficiency and crop yield, while minimizing manual intervention. Specific objectives include designing the system architecture integrating soil moisture sensors with control units, implementing the system in a low-cost hardware prototype, and evaluating its performance through field experiments. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative system usability assessment. The population comprises smallholder farmers and irrigation systems situated within the agricultural zones of the Central Valley, with a sample size of 50 farms selected via stratified random sampling to ensure representativeness. Data collection instruments include soil moisture sensors, programmable logic controllers (PLCs), farmers’ usability questionnaires, and crop yield records. The field trials are conducted over an entire growing season, with data collected on soil moisture levels, irrigation schedules, water consumption, and crop growth parameters. Quantitative data are analyzed using regression analysis to determine the relationship between soil moisture levels and irrigation needs, while ANOVA tests evaluate the significance of yield differences between systems. Thematic analysis is employed to interpret farmer feedback regarding system usability and acceptance. Expected findings suggest that the automated system maintains optimal soil moisture levels, resulting in a 30% reduction in water use compared to traditional manual irrigation, with statistically significant improvements (p < 0.05) in crop yields. The findings also anticipate enhanced operational efficiency, with system reliability exceeding 95%, and positive farmer perceptions regarding ease of use. These results are expected to confirm that integrating soil moisture sensors with automated controls can substantially improve water conservation and crop productivity in smallholder farming contexts. This research contributes to the body of knowledge by demonstrating a practical application of sensor-based automation tailored to small-scale farmers, addressing gaps related to affordability and contextual adaptation identified in existing literature. The theoretical framework incorporates the Diffusion of Innovations theory to understand technology adoption and the Control Systems theory to model irrigation regulation mechanisms. The study offers a novel, scalable prototype that combines low-cost hardware components with open-source software, thereby advancing the knowledge of sustainable irrigation solutions suitable for resource-constrained settings. The main conclusion underscores that the developed automated irrigation system effectively enhances water efficiency and crop productivity, affirming its potential as a sustainable agricultural intervention. The study recommends further refinement of the system to incorporate weather forecast integration and explore remote monitoring capabilities. It also advocates for extension services to facilitate wider adoption among smallholder farmers and suggests longitudinal studies to assess long-term impacts on water sustainability and economic viability. Ultimately, this research provides empirical evidence supporting the adoption of sensor-based automation to address critical water management challenges in agriculture and offers a foundation for future innovations in precision irrigation technologies.