loT- Based Sensor Applications for Water Quality Monitoring in Intensive Aquaculture Systems - Faiz Muqorrir Kaaffah; Khaerul Manaf; Beki Subaeki (International Research of Multidisciplinary Analysis, 2026)
Intensive aquaculture requires strict water quality monitoring because fluctuations in parameters such as temperature, pH, dissolved oxygen, and ammonia can cause stress and even mass mortality among farmed organisms. Accurate and timely monitoring is a critical operational requirement to ensure the sustainability and profitability of intensive aquaculture operations in meeting global demand for animal protein. This study aims to design, develop, and test a comprehensive, cost-effective IoT sensor system for real-time water quality monitoring in intensive aquaculture ponds. By integrating multi-parameter sensors into a platform connected to an automated aeration system, the system sends early warnings and action recommendations to farmers via a mobile app in areas with limited connectivity. The methodology employs a Systematic Literature Review (SLR), a systematic, explicit, and reproducible approach to identify, evaluate, and synthesize relevant empirical evidence from the scientific literature. The SLR produces a comprehensive and objective synthesis of research findings, minimizes bias, and provides an evidence-based foundation for decision-making, following a structured protocol for the critical evaluation of study quality and relevance. The discussion covers four categories: the basic implementation of an IoT system monitoring key parameters provides real-time data and early warnings to boost productivity at low cost—a game-changer that minimizes risks and reliance on manual labor; connectivity innovations using LoRa technology transmit sensor data without adequate WiFi or cellular infrastructure, eliminating adoption barriers for small-to-medium-scale farmers; commodity-specific parameter expansion using TDS and turbidity sensors achieving high accuracy with an error rate <0.5% for sensitive aquaculture such as shrimp farming; system integration and automated response demonstrating the integration of sensors with automatic feed-dispensing actuators to create a responsive and autonomous closed-loop system. This study concludes that IoT systems monitoring fundamental parameters such as temperature, pH, and DO are viable and provide significant benefits in the form of increased productivity through proactive management. LoRa technology successfully overcomes connectivity challenges in remote areas, expanding the scope of IoT applications. Monitoring specific parameters with high accuracy is key to cultivating sensitive commodities. The future of precision aquaculture lies in the integration of IoT monitoring with automated control to create a responsive and autonomous cultivation environment