Developing a IoT-based Sensor Network for Real-Time Soil Nutrient Monitoring

Thesis Abstract

Effective soil nutrient management is critical for optimizing agricultural productivity and ensuring sustainable land use, yet traditional methods for soil testing are often labor-intensive, time-consuming, and lack real-time data, leading to delayed decision-making and potential nutrient imbalances. This study aims to develop an Internet of Things (IoT)-based sensor network capable of providing real-time, continuous soil nutrient monitoring to facilitate timely and precise agricultural interventions. The specific objectives include designing and integrating low-cost, durable soil nutrient sensors with wireless communication modules, implementing a scalable IoT framework for data collection and transmission, and evaluating the system’s accuracy, reliability, and usability in a controlled agricultural setting. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative usability assessment. The quantitative component involves deploying a prototype sensor network across a sample of 50 plots within an agricultural research farm covering diverse soil types. Data collection instruments include soil sensors measuring key nutrients such as nitrogen, phosphorus, and potassium, integrated with Arduino-based microcontrollers and GSM modules for data transmission. The sensors are calibrated through laboratory analysis using ion chromatography and colorimetric techniques to establish baseline accuracy. Data analysis employs regression analysis to ascertain correlations between sensor readings and laboratory results, and ANOVA tests to assess variance across different soil types. The qualitative component involves semi-structured interviews with 15 agronomists and farmers to evaluate system usability and practical adoption barriers, analyzed thematically. Expected findings indicate that the IoT sensor network achieves high correlation coefficients (r > 0.85) with laboratory measurements, demonstrating strong validity and reliability. The system is anticipated to significantly reduce the time lag associated with traditional soil testing, providing real-time data that enhances decision-making regarding fertilization and crop management. Variability in sensor performance across different soil types is expected, highlighting the necessity for specific calibration protocols. Qualitative insights are likely to reveal key usability factors, including system intuitiveness, data accessibility, and technical support requirements, which influence potential adoption. This study contributes to knowledge by demonstrating the feasibility and effectiveness of deploying IoT sensor technologies for soil nutrient monitoring in agricultural practice, filling existing gaps related to real-time data provision and sensor calibration in heterogeneous soils. It advances existing models of digital agriculture by integrating sensor networks within decision support frameworks, aligned with the Theory of Diffusion of Innovations and Technological Acceptance Model (TAM). The findings provide a framework for scalable implementation in diverse farming environments, emphasizing the importance of user-centered design and system robustness. The main conclusion underscores the potential of IoT-based sensor networks to revolutionize soil fertility management through increased data accuracy and accessibility, fostering sustainable farming practices. Recommendations include developing standard calibration procedures for different soil conditions, expanding sensor deployment to larger spatial scales, and integrating the network with existing farm management systems. Future research should explore long-term system stability, cost-benefit analyses, and the integration of additional environmental sensors to enhance monitoring comprehensiveness, ultimately contributing toward smarter, more resilient agricultural systems.