IoT Water Quality Monitoring for Aquaculture (Tilapia Fish Farms)
Catalog Summary
"Six months of IoT sensor data from tilapia aquaculture ponds in Colombia. Monitors dissolved oxygen, pH, temperature, turbidity with fish health metrics (weight, survival rate, disease occurrence)."
Catalog Notes
Dataset Overview
Comprehensive dataset from real-world aquaculture operations capturing the relationship between water quality parameters and tilapia (Oreochromis niloticus) fish health. Collected using IoT sensors deployed in aquaculture ponds in Montería, Colombia, designed for predictive modeling and sustainable fish farming management.
Water Quality Parameters
- Dissolved Oxygen (DO): Critical for fish respiration measured in mg/L
- pH Level: Water acidity/alkalinity (0-14 scale) affecting nutrient availability
- Temperature: Water temperature in °C influencing fish metabolism and growth
- Turbidity: Water clarity measured in Nephelometric Turbidity Units (NTU)
Fish Health Indicators
- Average Fish Weight: Growth tracking in grams
- Survival Rate: Percentage of fish survival during monitoring period
- Disease Occurrence: Number of disease cases observed
- Oxygenation Interventions: Whether artificial oxygenation was applied (Yes/No)
- Corrective Interventions: Number of corrective measures taken
Data Collection Method
- Duration: 6 months continuous monitoring (January-June 2024)
- Sampling Frequency: Every 6 hours throughout monitoring period
- IoT System: Raspberry Pi-based with calibrated sensors (ISO 5814:2012, ISO 10523:2008)
- Data Transmission: Wi-Fi connectivity with Django web interface for real-time visualization
Research Applications
Predictive modeling for water quality management using ML algorithms (Random Forest, SVM), automated aquaculture decision support systems, fish mortality reduction strategies, sustainable aquaculture practices, and rural fish farming optimization.
Data Preview
| Month | Avg_Fish_Weight_g | Survival_Rate_% | Disease_Cases | Temperature_C | Dissolved_Oxygen_mg_L | pH | Turbidity_NTU | Oxygenation_Applied | Corrective_Interventions |
|---|---|---|---|---|---|---|---|---|---|
| January | 250 | 95.2 | 2 | 28.5 | 6.8 | 7.2 | 12.5 | No | 0 |
| February | 285 | 94.8 | 3 | 29.1 | 5.9 | 7.0 | 15.8 | Yes | 2 |
| March | 320 | 96.5 | 1 | 27.8 | 7.1 | 7.3 | 10.2 | No | 0 |
Showing first few rows for preview
Provided Citation
Jocelyn Dumlao (2024). IoT Monitoring of Water Quality and Tilapia. Kaggle. Retrieved from https://www.kaggle.com/datasets/jocelyndumlao/iot-monitoring-of-water-quality-and-tilapia
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Source metadata: Kaggle (2026)
Indexed by IoTDataset.com on Jan 14, 2026
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