AquaGuardian: An IoT-driven real time water quality monitoring and machine learning prediction for aquaculture systems in Uganda
| dc.contributor.author | Calvin Tendo | |
| dc.contributor.author | Ronald Austine Ezamamti | |
| dc.contributor.author | Emmanuel Kisa | |
| dc.date.accessioned | 2026-08-17T12:40:35Z | |
| dc.date.available | 2026-08-17T12:40:35Z | |
| dc.date.issued | 2026-06-08 | |
| dc.description | Undergraduate | |
| dc.description.abstract | Uganda’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. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/3536 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | AquaGuardian: An IoT-driven real time water quality monitoring and machine learning prediction for aquaculture systems in Uganda | |
| dc.type | Project Report |