2026· Journal of Computational Systems and ICTs· 0 citations· 2 references
TL;DR
It is concluded that IoT-ML integration has demonstrated technical feasibility, but further applied research is required for effective adoption in commercial aquaculture farms, particularly in resource constrained rural settings where most small-scale fish farming occurs.
Abstract
This systematic review analyzes 52 studies (2018–2026) on integrated
IoT and Machine Learning systems for water quality prediction in
aquaculture. Results show that Random Forest achieves accuracy above
94 % in survival classification, while hybrid Deep Learning models such
as CNN-SA-BiSRU reach R² = 0.9765 for dissolved oxygen prediction.
The most monitored parameters are pH (98.2 %), temperature (92.9 %),
and dissolved oxygen (62.5 %). Critical gaps are identified: scarcity of
species-specific datasets for larvae and fry, predominance of single
target models, and limited validation under real production conditions.
The review concludes that IoT-ML integration has demonstrated
technical feasibility, but further applied research is required for effective
adoption in commercial aquaculture farms, particularly in resource
constrained rural settings where most small-scale fish farming occurs.
This survey explores recent innovations in IoT-based Water Quality Monitoring Systems (IoT-WQMS) integrated with Machine Learning (ML) and Deep Learning (DL) to enable real-time, automated water quality assessment.
Kumar S. Ashok, Radhakrishnan C. V.· International Journal of Sci...· 0 citations
Water resource management has become an increasingly critical issue due to rapid urbanization, population growth, climate variability, and industrial expansion. Ensuring a sustainable water supply requires not only effective distribution but also accurate prediction of future water demand and continuous monitoring of w...
In modern agriculture, the quality of irrigation water plays a crucial role in determining crop productivity and soil health. With increasing reliance on precision farming methods, there is a growing need for intelligent systems that can continuously monitor and predict water quality to ensure safe and efficient usage....
M. Parvathi· Natural Resources for Human...· 0 citations
A digital hydrology model driven by deep learning capabilities, incorporating IoT-based water quality monitoring with advanced artificial neural networks to estimate the water quality index is proposed, finding that the performance of CNN–LSTM significantly outperforms standard LSTM with lower prediction errors and hig...
Akash Rajak, Sunil Kumar, SiddheshwariDutt Mishra et al.· Journal of Communications So...· 0 citations
The key finding is that hybrid and attention-enhanced deep learning architectures are frequently reported to outperform single architecture baselines in the reviewed literature and offer the greatest promise for robust, interpretable, and scalable systems for predicting water quality.
W. W. Wan Ismail, Abdallah Abuishaq, Nor Azlina Bt Ab. Aziz et al.· Environments· 0 citations
Climate change is having an increasing impact on agriculture, leading to erratic weather patterns and reduced crop yield. Traditional forms of farming do not always solve these problems. In an attempt to aid intelligent farming decisions, this paper proposes a smart agriculture system based on deep learning and the Int...
S. Kumar, M. Akshath, Ponna Vishal et al.· Engineering & Technology· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
