IoT Monitoring Dataset of Water Quality and Tilapia (Oreochromis niloticus) Health in Aquaculture Ponds in Montería, Colombia (2024))

Published: 5 November 2024| Version 1 | DOI: 10.17632/3g2b4sh65m.1
Contributors:
Rubén Baena-Navarro,
,

Description

Description This dataset contains six months of water quality and tilapia (Oreochromis niloticus) health monitoring data collected from aquaculture ponds in Montería, Colombia. Using an IoT-based monitoring system, critical parameters such as dissolved oxygen (DO), pH, water temperature, and turbidity were recorded. Fish health indicators, including average fish weight and survival rate, are also included. Data was collected from January to June 2024, with hourly readings to capture daily fluctuations and ensure comprehensive monitoring of aquaculture conditions and tilapia well-being. ________________________________________ Included Files 1. Data Model IoTMLCQ 2024.xlsx o Contains sensor readings and fish health data collected over six months. o Columns:  Datetime: Date and time of each reading.  Month: Data collection month (January to June).  Average Fish Weight (g): Average weight of the tilapia fish in grams.  Survival Rate (%): Percentage of fish survival during the monitoring period.  Disease Occurrence (Cases): Number of disease cases observed.  Temperature (°C): Water temperature readings.  Dissolved Oxygen (mg/L): Levels of dissolved oxygen in the water.  pH: Water pH values.  Turbidity (NTU): Water turbidity measured in Nephelometric Turbidity Units (NTU).  Oxygenation Automatic: Indicates if automatic oxygenation was applied (Yes/No).  Oxygenation Interventions: Oxygenation interventions applied (Yes/No).  Corrective Interventions: Number of corrective measures taken.  Thermal Risk Index: Indicates if the thermal risk is "High" or "Normal."  Low Oxygen Alert: Shows "Critical" if DO levels are below 5 mg/L, otherwise "Safe."  Health Status: Fish health status, showing "At Risk" or "Stable" based on thermal and oxygen risk alerts. ________________________________________ Data Collection Method Data was collected using IoT sensors strategically placed in the aquaculture ponds. Readings were taken every hour throughout the monitoring period. This dataset provides valuable insights into the relationship between water quality parameters and the health of tilapia (Oreochromis niloticus) in controlled aquaculture conditions. ________________________________________ Usage Notes • This dataset is useful for research in aquaculture management, water quality monitoring, and predictive modeling of fish health and growth. • Missing data due to sensor or communication failures were addressed using interpolation methods. • Regular sensor calibrations were performed to ensure accuracy in the collected data.

Files

Steps to reproduce

To reproduce the data, an IoT-based monitoring system was deployed in aquaculture ponds located in Montería, Colombia. The system included IoT sensors strategically positioned in the ponds to measure critical water quality parameters such as dissolved oxygen (DO), pH, water temperature, and turbidity. Sensors recorded data every hour, capturing variations in water quality throughout each day. The following protocols and equipment were used: • Sensors: Calibrated IoT sensors specific to each parameter (e.g., DO, pH, temperature, turbidity). • Data Collection Protocol: Sensors were set to log readings hourly from January to June 2024. • Calibration and Maintenance: Sensors were regularly calibrated to ensure accuracy, and any sensor or communication failures were addressed with interpolation techniques. • Data Handling: Data was processed and cleaned to ensure no empty cells, with missing values addressed through interpolation. This workflow, combining regular sensor calibration and hourly data logging, provides a comprehensive dataset for monitoring fish health and water quality in aquaculture settings.

Institutions

Universidad Cooperativa de Colombia, Universidad de Cordoba

Categories

Environmental Science, Aquaculture, Data Science, Machine Learning, Internet of Things, Water Quality

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