Smart Irrigation Monitoring System Using Remote Sensing and IoT Technologies

Thesis Abstract

Efficient water management remains a critical challenge in modern agriculture, particularly in regions experiencing irregular rainfall patterns and water scarcity, necessitating innovative solutions that optimize irrigation practices. This study aims to develop and evaluate a Smart Irrigation Monitoring System that leverages remote sensing technologies and Internet of Things (IoT) devices to provide precise, real-time irrigation management, thereby enhancing water use efficiency and crop productivity. The specific objectives include designing an integrated system that utilizes satellite-derived soil moisture data and weather forecasts, deploying IoT-based sensor networks for localized soil and environmental monitoring, and implementing data analytics models to inform irrigation scheduling. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative system validation. The population comprises smallholder farmers within agricultural districts characterized by variable climatic conditions, totaling approximately 200 farms. A stratified random sampling technique selects a sample of 80 farms to ensure representativeness across different farm sizes and crop types. Data collection instruments include satellite imagery datasets obtained from Sentinel-2 satellites, IoT sensors measuring soil moisture, temperature, and humidity, and structured questionnaires for farmers’ feedback. System performance data are collected over one full growing season (approximately six months), with the IoT sensor readings transmitted wirelessly to a cloud-based analytics platform. Quantitative data are analyzed using regression analysis to assess relationships between soil moisture levels, weather variables, and crop yield, complemented by time-series analysis to evaluate irrigation scheduling efficiency. Qualitative data from farmers’ interviews are thematically analyzed to explore user perceptions and system acceptance. The analytical framework integrates the Theory of Planned Behavior and Diffusion of Innovations theory to understand adoption drivers and barriers. The study employs Geographic Information System (GIS) tools to visualize spatial variability in soil moisture and water usage patterns. Expected findings indicate that the integrated remote sensing and IoT system significantly improves irrigation accuracy, reduces water wastage by up to 30%, and increases crop yields by an average of 15% compared to traditional practices. The research anticipates demonstrating that farmers' adoption is positively influenced by perceived ease of use and relative advantage, emphasizing the importance of user-centered technology design. The system’s predictive models are expected to exhibit high accuracy (above 85%) in estimating optimal irrigation timing, validating the integration of satellite data with ground sensors. This study contributes to the body of knowledge by providing empirical evidence on the effectiveness of a multi-sensor, ICT-driven approach for sustainable water management in agriculture. It advances existing frameworks by integrating remote sensing data with IoT sensor networks within a unified decision-support system, filling gaps identified in prior studies regarding system scalability and user acceptance. Additionally, the research offers a replicable model for developing context-specific smart irrigation systems in similar arid and semi-arid regions. The main conclusion underscores that the implementation of a smart irrigation monitoring system rooted in remote sensing and IoT significantly enhances water use efficiency, crop productivity, and farmers’ adaptive capacity to climate variability. Based on findings, recommendations include policy support for ICT infrastructure development in rural areas, capacity building for farmers on smart system utilization, and fostering partnerships between technological developers and agricultural extension services. Future research should explore long-term adoption dynamics, economic feasibility analyses, and the integration of additional data sources such as drone imagery and machine learning algorithms to further refine irrigation management practices.