AIoT-Enabled Real-Time Water Quality and Fish Health Monitoring System for Smart Aquaculture

DOI

10.2991/978-94-6239-664-7_81How to use a DOI?

Keywords

AIoT; Smart Aquaculture; Water Quality Monitoring; Dissolved Oxygen Prediction; Edge Computing; Arduino; Raspberry Pi; Machine Learning

Abstract

This study introduces a unified AIoT-enabled smart aquaculture system that integrates real-time water-quality monitoring, dissolved-oxygen (DO) prediction, and fish-disease detection within a single, low-cost, field-deployable framework. The system employs Arduino sensors to measure temperature, pH, and turbidity, combined with Raspberry Pi edge computing for continuous data acquisition and on-device analytics. A regression model predicts DO levels from low-cost sensor inputs, eliminating the need for expensive probes, while water quality is classified into three categories using machine-learning approaches. The baseline SVM model achieves 79% accuracy, whereas advanced ensemble methods improve performance to 92.46%. Fish health assessment utilizes a curated dataset of 1,000 veterinarian-verified images, augmented to 3,000 samples. Deep CNN models exhibit strong discriminative performance, with ResNet50 achieving 97.5% accuracy and surpassing MobileNetV2. The full pipeline operates efficiently on a Raspberry Pi, with an average end-to-end inference latency of 1.8 s, enabling automated control of pumps and aerators based on real-time predictions. A mobile dashboard provides live visualization, alerts, and historical data tracking for remote farm supervision. Extensive field validation across 160 aquaculture farms, encompassing over 5,500 sensor records, demonstrates the system’s robustness, scalability, and adaptability to diverse environmental conditions.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Abu Kausar
AU  - Md Shamsuzzaman Mia
AU  - Md.Salah Uddin
AU  - Kazi Jahid Hasan
AU  - Amir Hossen
AU  - MD.Humaun Kabir
PY  - 2026
DA  - 2026/06/08
TI  - AIoT-Enabled Real-Time Water Quality and Fish Health Monitoring System for Smart Aquaculture
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1197
EP  - 1213
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_81
DO  - 10.2991/978-94-6239-664-7_81
ID  - Kausar2026
ER  -