Smart Irrigation Scheduling using IoT and Soil Sensing for Resource Efficiency

Thesis Abstract

This study addresses the escalating demand for water-use efficiency in agriculture by investigating an IoT-enabled soil sensing system to optimize irrigation scheduling under diverse agro-ecological conditions. The central aim is to develop, validate, and operationalize a real-time irrigation scheduling framework that integrates soil moisture, weather data, crop water requirements, and plant-available water via an embedded decision-support algorithm. Specific objectives include (i) designing a low-power IoT network for continuous soil moisture and microclimate monitoring across heterogeneous fields; (ii) formulating a cloud-based analytics platform that fuses sensor data with crop coefficients to predict irrigation needs; (iii) evaluating the performance of rule-based versus model-driven scheduling strategies against conventional irrigation practices; and (iv) assessing resource outcomes (water savings, yield, and quality) and energy implications of the proposed system. The methodological approach employs a mixed-methods design grounded in the Technology Acceptance Model and the Diffusion of Innovations theory to understand performance and adoption dynamics. The population comprises commercial row-crop farms in a temperate climate region, with twelve fields selected through stratified purposive sampling to capture variability in soil textures, irrigation infrastructure, and crop types. A total of 1200 soil-moisture sensors (capacitance-based, ±2% volumetric water content accuracy) and meteorological stations are deployed across the study sites, with data collected at 15-minute intervals over two full growing seasons. Instrumentation includes calibrated soil moisture probes, solar-powered IoT gateways, weather sensors (precipitation, solar radiation, temperature, humidity), and a crop-growth database integrated into a remote server. Reliability and validity are ensured through pre-deployment calibration, cross-validation against gravimetric soil samples, and pilot testing of the data pipeline. Data analysis proceeds in three layers. First, descriptive statistics characterize sensor performance, data completeness, and baseline irrigation practices. Second, inferential analyses employ repeated-measures ANOVA to compare water use efficiency, crop yield, and quality indicators between the IoT-driven system and conventional scheduling, controlling for soil type and weather variance. Multivariate regression models identify key predictors of irrigation demand and quantify the marginal effects of soil moisture setpoints, soil texture, and climate variables. Third, the temporal forecasting component uses a hybrid approach combining a process-based soil water balance model with machine-learning residual corrections (LASSO and random forest) to generate site-specific irrigation recommendations. Model specification includes calibration against observed evapotranspiration, runoff, and deep percolation losses. Additionally, a cost-benefit analysis evaluates operational expenditures, energy consumption, and farm profitability. Expected findings indicate substantial reductions in water use (15–25%), with consistent yields and no detrimental effect on grain or fruit quality relative to traditional scheduling. The study also anticipates improved scheduling precision during critical growth stages (reproductive phase), accompanied by lower energy intensity per hectare due to optimized pump times. The analytics are expected to reveal that incorporating site-specific soil hydraulic properties and near-real-time weather cues significantly enhances irrigation accuracy compared to fixed schedules. The research contributes to knowledge by integrating IoT-enabled sensor networks with a robust decision-support model tailored for heterogeneous field conditions and by demonstrating scalability across crop systems. The study’s contribution to knowledge encompasses (i) a validated, scalable framework for smart irrigation that combines low-cost sensors, edge computing, and cloud analytics; (ii) empirical evidence on water savings, yield stability, and energy efficiency under model-driven scheduling; (iii) an enhanced understanding of adoption dynamics for IoT-based irrigation management among commercial farmers; and (iv) practical guidelines for policymakers and engineers on system deployment, data governance, and farmer training. The conclusion emphasizes that IoT-driven soil sensing with adaptive scheduling can achieve resource efficiency without compromising productivity, underlining the importance of site-specific calibration, user-centered interface design, and ongoing maintenance. Recommendations include expanding to multi-crop platforms, integrating remote sensing data for drought monitoring, developing modular cost structures to accelerate adoption, and establishing standardized benchmarks for irrigation performance across agro-ecologies.