Development of a mobile app for real-time soil nutrient monitoring and management

Thesis Abstract

Effective soil nutrient management is critical for optimizing agricultural productivity and ensuring sustainable land use amidst increasing environmental challenges. However, farmers and agronomists often lack access to timely, precise, and affordable soil nutrient data, which hampers informed decision-making regarding fertilization strategies. This study aims to facilitate real-time soil nutrient monitoring and management through the development of a mobile application that integrates sensor data, geolocation, and analytical algorithms. The specific objectives are to design a user-friendly mobile app capable of collecting and interpreting soil nutrient data, evaluate the app’s accuracy against laboratory-based soil analysis, and assess its usability and potential impact on farm management practices. The research adopts a mixed-methods approach, combining quantitative and qualitative methodologies to ensure comprehensive evaluation. Quantitatively, a comparative analysis involves deploying soil nutrient sensors across a sample of 100 agricultural plots within a designated farming region to collect real-time data. These sensor-based readings are validated through laboratory soil analyses conducted on samples from the same sites, utilizing standard techniques such as inductively coupled plasma mass spectrometry (ICP-MS) for nutrient determination. The app’s performance in nutrient estimation is analyzed through regression analysis, comparing sensor and laboratory data, with models developed to enhance prediction accuracy. Qualitative data are gathered via focus group discussions and structured questionnaires administered to 50 farmers and extension officers to evaluate usability, perceived usefulness, and acceptance, analyzed through thematic analysis. Expected findings indicate that the mobile app can accurately aggregate and interpret soil nutrient data, with sensor readings showing a statistically significant correlation (p<0.05, R2>0.80) with laboratory results after calibration. The app is anticipated to significantly improve access to soil data for farmers, leading to more precise fertilizer application and enhanced crop yields, as evidenced by increased nutrient use efficiency and reduced input costs in field trials. Usability assessments are expected to reveal high acceptance levels owing to the app’s intuitive interface, customized recommendations based on farm-specific data, and integration with existing farm management practices. This study contributes to the body of knowledge by demonstrating how ICT-driven solutions can bridge critical gaps in soil information systems, supporting sustainable agriculture and resource use. The integration of sensor technology, geospatial data, and predictive analytics within a mobile platform offers a scalable model adaptable to diverse agro-ecological zones. Theoretically, the study builds upon the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory to elucidate factors influencing adoption and sustained use of the mobile app within farming communities. The main conclusion underscores the potential of mobile technology in transforming soil nutrient management into a more accurate, timely, and accessible practice. Recommendations include further refinement of sensor calibration algorithms to improve accuracy, expansion of the app’s features to include pest and disease management modules, and strategies for widespread adoption through stakeholder engagement and capacity building. Future research should explore longitudinal impacts on agricultural productivity, environmental sustainability, and policy integration to foster broader systemic change. This study thereby offers a practical, evidence-based framework for harnessing ICT to support sustainable and precision agriculture at the grassroots level.