Developing AI-Driven IoT Sensor Networks for Precision Crop Nutrition

Thesis Abstract

This study addresses the critical challenge of achieving precise nutrient management in crops through intelligent, scalable sensor-based agronomic practices. Despite advances in digital agriculture, nutrient use efficiency remains suboptimal in many cropping systems due to heterogeneous soil properties, variable microclimates, and delays in interpretation of sensor data. The aim is to develop and validate an AI-driven Internet of Things (IoT) sensor network capable of delivering site-specific nutrient recommendations to optimize yield, quality, and environmental sustainability. The specific objectives are to (1) design a modular IoT sensor network integrating soil moisture, soil nutrient proxies, leaf chlorophyll indices, microclimate, and crop growth stage data; (2) develop edge- and cloud-based machine learning models that infer spatial-temporal nutrient needs and forecast fertilizer requirements; (3) implement closed-loop decision support with automated irrigation and fertigation control in pilot plots; (4) evaluate model performance across diverse soil types and cropping systems; and (5) assess economic viability and environmental impact using life cycle assessment and farmer adoption metrics. The methodology adopts a mixed-methods, iterative design research approach underpinned by the Technology Acceptance Model and the Theory of Planned Behavior to examine adoption and performance. The population comprises commercial irrigated maize and wheat fields across three agro-ecological zones. A multi-stage sampling plan selects 20 fields (approximately 40–60 hectares total) with contrasting soil textures and fertility histories. The IoT sensor network comprises 64 soil moisture and nutrient proxies, 32 leaf chlorophyll sensors, 16 microclimate stations, and 8 proximal nutrient spectrometers, deployed over a full growing cycle in each crop. Data collection instruments include calibrated soil probes (NH4+, NO3?, P, K), high-resolution multispectral imagery, drone-based biomass estimates, and fertilizer application logs. Ground-truth fertilization and yield data will be recorded to validate model outputs. The data analysis framework integrates statistical and machine learning methods data preprocessing and imputation, spatial-temporal modelling with Gaussian process regression, gradient boosting for nutrient status prediction, and recurrent neural networks for growth stage forecasting. Model specifications include a hierarchical Bayesian approach to quantify uncertainty in nutrient recommendations, and an ensemble of models validated by cross-validation and out-of-sample testing. Feature selection employs SHAP values to interpret model decisions and identify key drivers of nutrient requirements. A pilot implementation uses a closed-loop control system linking the AI model outputs to an automated fertigation unit and irrigation valve actuators, with real-time data feedback to the cloud platform. Economic analysis combines net present value, return on investment, and sensitivity analysis; environmental impact is assessed via life cycle assessment focusing on fertilizer use and nitrate leaching potential. Expected findings include (i) robust, generalizable AI models that predict site-specific fertilizer needs with mean absolute error within 10–15% of measured requirements; (ii) demonstrable reductions in total fertilizer input (10–25%) without yield loss, alongside improvements in crop nitrogen use efficiency and leaf N status; (iii) validated closed-loop control that maintains soil nutrient status within target ranges across heterogeneous fields; and (iv) evidence of favorable adoption potential among farmers, with identified barriers and enablers. The study contributes to knowledge by integrating IoT sensing, real-time analytics, and autonomous agronomic decision-making, advancing precision nutrition paradigms under variable field conditions. Theoretical implications include empirical support for hybrid AI-physical control loops in agriculture and refinement of the Theory of Planned Behavior for technology-driven agronomic adoption. Practical implications encompass scalable templates for sensor deployment, data governance, and cost-benefit frameworks for precision nutrition interventions. The main conclusion anticipated is that AI-driven IoT sensor networks can deliver accurate, timely, and economically viable nutrient management recommendations, enabling farmers to optimize yield and quality while reducing environmental footprints. Recommendations include expanding sensor coverage to cover additional micronutrients, refining user interfaces to support end-user decision-making, developing standardized data schemas for cross-region interoperability, and conducting longitudinal studies to assess long-term soil health impacts and policy implications related to nutrient management.