A Sustainable Framework for Precision Irrigation System Optimization Using IoT Data

Thesis Abstract

Efficient water management in agriculture remains a critical challenge amid increasing water scarcity and climate variability, necessitating innovative solutions that enhance irrigation efficiency and sustainability. This study aims to develop a comprehensive and sustainable framework for optimizing precision irrigation systems utilizing Internet of Things (IoT) data. The specific objectives include identifying key IoT-enabled parameters influencing irrigation efficiency, designing and implementing an integrated data-driven model for real-time decision-making, evaluating the environmental and economic impacts of the proposed framework, and establishing guidelines for sustainable irrigation management in diverse agricultural contexts. The research adopts a mixed-methods design, combining quantitative modeling techniques with qualitative assessments to generate a robust, holistic framework. The population comprises 50 commercial farms engaged in crop production within the Central Valley region, selected through stratified random sampling to ensure representation of various crop types and farm sizes. A sample of 150 IoT sensors—comprising soil moisture sensors, weather stations, and flow meters—were deployed across the selected farms over one growing season, facilitating continuous data collection. Data collection instruments include IoT sensor networks, digital questionnaires, and semi-structured interviews with farm managers and agronomists. Quantitative data obtained from the sensors were analyzed using regression analysis to identify the relationships between environmental variables and irrigation performance. Principal component analysis (PCA) was employed to reduce data dimensionality and identify the most influential parameters for system optimization. The decision-making model was developed utilizing a multi-criteria decision analysis (MCDA) framework, integrated with machine learning algorithms such as Random Forests to predict optimal irrigation schedules under variable conditions. Thematic analysis was conducted on qualitative data to understand stakeholders’ perceptions, constraints, and adoption barriers, informing the framework's contextual applicability. Expected findings indicate that specific IoT parameters—such as soil moisture levels, evapotranspiration rates, and real-time weather data—significantly influence irrigation efficiency. The integrated model is anticipated to demonstrate improved water savings, estimated at up to 30% compared to traditional practices, alongside enhanced crop yields and reduced runoff. The framework is envisaged to be adaptable across different crop systems and climatic zones, contributing to sustainable water use and climate-resilient agriculture. The study advances knowledge by providing a novel, empirically validated framework that leverages sensor data and advanced analytical techniques to inform sustainable irrigation practices. It enriches existing theoretical foundations based on the Diffusion of Innovation theory and the Resource-Based View, demonstrating how technological integration can be effectively harnessed for sustainable resource management. The main conclusion underscores that IoT-enabled data-driven decision-making significantly enhances irrigation sustainability when embedded within a comprehensive, context-sensitive framework. Policy and practice recommendations include promoting wider adoption of IoT technologies tailored to local conditions, integrating the framework within extension services, and providing capacity-building initiatives for farmers. The study also suggests further research into long-term impacts, scalability across different regions, and integration with other smart farming systems. Overall, this research contributes to the development of science-based, sustainable irrigation management strategies that can inform policy and foster resilient agricultural systems amid evolving environmental challenges.