ArticleSensors (Basel, Switzerland)2025
Machine Learning on Low-Cost Edge Devices for Real-Time Water Quality Prediction in Tilapia Aquaculture.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Climate-mediated food safety hazards of aquaculture.Environmental research. Food systems · 2026Article
- Preliminary Field Evaluation of a Low-Cost IoT Workflow for Dissolved Oxygen Monitoring and Short-Horizon Forecasting in Nile Tilapia Aquaculture.Sensors (Basel, Switzerland) · 2026Article
- SQUID-COMM: a Colossal Squid-inspired distributed communication framework for real-time multi-node aquaculture monitoring networks with adaptive bioluminescent signaling and neuromorphic edge intelligence.Scientific reports · 2026Article
- Progress of Rapid Detection Technology for Aquatic Microorganisms: A Comprehensive Review.Microorganisms · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models-Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)-were evaluated. RFR achieved the highest accuracy (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.