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Aqua-Advisor: Cost-Effective Real-Time Fish Recommendation Using Predicted Dissolved Oxygen via IoT and Machine Learning

Students & Supervisors

Student Authors
Jafir Islam Siam
Bachelor of Science in Computer Science & Engineering, FST
Md.rafiul Islam
Bachelor of Science in Computer Science & Engineering, FST
Farhina Parvin Swarna
Bachelor of Science in Computer Science & Engineering, FST
Jannatul Maowa Hossain Meem
Bachelor of Science in Computer Science & Engineering, FST
Md. Abdullah Ibna Sina
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Md Sajid Hossain
Assistant Professor, Faculty, FE

Abstract

Specifying fish species based on the water quality conditions is a crucial measure in sustainable aquaculture. How ever, smallholder-level farmers usually have no way of accessing real-time environmental data. The lack of proper matching between species and their environments results in poor growth, high mortality rates, and economic losses. To overcome this, we introduce a low-cost IoT-based decision support system as an aid to select the appropriate fish species with respect to real time water quality data. This system includes a low- cost ESP32 microcontroller and temperature sensors, as well as a pH sensor that will gather real-time data, which are then used to predict Dissolved Oxygen (DO) by performing a linear regression model. Instead of using costly DO sensors, this method manages to provide fairly accurate results with a mean absolute error (MAE) of 0.42 mg/L. Afterwards, the temperature, pH, and predicted DO are fed into a rule-based engine that provides real-time fish recommendations through a Telegram bot. This solution, being designed around accessibility, aligns with UN Sustainable Development Goals (SDGs), especially SDG 2 (Zero Hunger) through efficient food production, SDG 9 (Industry, Innovation, and Infrastructure) by using IoT in the form of low-cost adoption, and SDG 14 (Life Below Water), by promoting sustainable aquaculture. Although the precision is still to be improved, this study shows that scalable data- driven solutions hold promise in leaning towards efficient small- scale farming in resource constrained environments, connecting traditional farming with smart aquaculture

Keywords

Aquaculture fish recommendation systems Dis solved Oxygen estimation the internet of things (IoT) machine learning sustainable development and water quality monitoring.

Publication Details

  • Type of Publication:
  • Conference Name: International Conference on Computer and Information Technology (ICCIT) 2025
  • Date of Conference: 19/12/2025 - 19/12/2025
  • Venue: Long Beach Hotel, Cox’s Bazar
  • Organizer: IEEE Bangladesh Section
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