Calvin TendoRonald Austine EzamamtiEmmanuel Kisa2026-08-172026-08-172026-06-08https://hdl.handle.net/20.500.12311/3536UndergraduateUganda’s aquaculture sector faces a critical productivity crisis, with national fish production declining by 27.8% and export revenue falling 21.9% between 2023 and 2024. This decline is largely attributed to inadequate real-time water quality monitoring among smallholder farmers, who continue to rely on manual testing methods that fail to detect sudden changes in critical parameters. This project presents AquaGuardian, an IoT-based smart fish pond monitoring and management system designed specifically for the Ugandan context. The system integrates a Raspberry Pi 4B controller with an integrated RS485 NPK sensor probe, a digital turbidity sensor and a GSM-based SMS alert module to enable continuous, real-time environmental surveillance. The architecture comprises five functional layers: Sensing, Processing, Actuation, Communication and Cloud Logging. A Random Forest machine learning model, trained on 3,887 samples from the Aquaculture Water Quality Dataset (AWD), classifies pond water quality into three categories: Excellent, Good and Poor. By utilizing biologically-informed feature engineering derived from NPK, pH and temperature data, the model achieved an 85.5% theoretical accuracy and 100% precision on the critical ”Poor” water quality class. Field testing validated the system’s ability to deliver real-time sensor visualization via a cloudhosted Flask dashboard deployed on Render and Neon.tech. The system successfully activated automated aeration and circulation pumps within five seconds of detecting a critical threshold and dispatched SMS alerts to farmers within thirty seconds. However, the evaluation also highlighted the limitations of using Nitrogen-based surrogate substitution for Nitrite inputs, a necessary compromise due to local hardware market constraints. This project demonstrates how the integration of IoT, machine learning and cloud technologies can bridge the monitoring gap in smallholder aquaculture,while providing a transparent assessment of the hardware-software alignment challenges encountered in resource-constrained environments.enProject Report