AQUAFOLIA: Low-Cost IoT Framework for Real-Time Monitoring and Predictive Management in Aquaponics
Bibliographic record
Abstract
Aquaponics has become a promising solution for sustainable and efficient food production, but its success depends greatly on proper monitoring and maintenance of water quality. Traditional manual testing methods are often slow, laborious, and prone to human error, creating the need for an automated and dependable system. To address this, the study introduces AQUAFOLIA, an Internet of Things (IoT) based framework designed to monitor and manage aquaponics systems in real time. The model continuously tracks essential water quality parameters and provides predictive insights to enhance performance. It uses affordable sensors connected to an Arduino UNO microcontroller, supported by an ADS1115 ADC for improved accuracy. Data collected from the sensors are sent to the ThingSpeak cloud platform for analysis and visualization. Hardware simulations were carried out using Proteus 8 Professional, while communication stability was verified through VSPE. The results confirmed successful integration of sensors, reliable data transmission, and accurate cloud monitoring. Measurements of pH (1.60-1.66), dissolved oxygen ($1.72-1.77 \mathrm{mg} / \mathrm{L}$), electrical conductivity $(1.68-1.73 \mathrm{mS} / \mathrm{cm})$, and temperature $\left(14-22{ }^{\circ} \mathrm{C}\right)$ stayed within optimal operational ranges. Interactive dashboards were developed to visualize control processes, parameter changes, time series trends, and historical data, emphasizing the system’s predictive capability. Along with monitoring, AQUAFOLIA also supports yield prediction, harvest planning, and species recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".