Optimizing Brackishwater Shrimp Farming with IoT-Enabled Water Quality Monitoring and Decision Support System

Abstract

Aquaculture plays a crucial role in coastal communities’ food, employment, and prosperity. Advancements in technology calls for real-time water quality monitoring for sustainable farming. This study aims to develop an IoT-based Analytical Hierarchy Process Decision Support System (AHP-DSS) for water quality monitoring in brackishwater shrimp farming. Two IoT-based models were developed with Raspberry pi3 and Arduino Mega2560 as microcontrollers with water quality sensors viz. pH, DO, temperature, turbidity, and salinity. AHP-based weightage was generated using the relative importance of each criterion for shrimp farming. Water quality variables were classified into highly suitable, moderately suitable, and not suitable for shrimp farming. Remote and real-time monitoring was done using Android and web applications. The water quality score for the developed model is scaled from 0–100. A comparative assessment was carried out for IoT installed pond and pond with laboratory water quality analysis. IoT installed pond provides low FCR compared to traditional water quality analysed pond. The system provides an alert when the water quality reaches below the moderately suitable threshold value. The developed system was tested in MES, ICAR-CIBA shrimp ponds, it was found to be highly useful, and it would improve shrimp production by maintaining optimum water quality in shrimp pond.