IOT Based Bio-Floc Water Quality Optimization System for Sustainable Shrimp Aquaculture

Muralidharan V 1, Munipandeeswaran P 2 ,  Agesta Jenifer A 3

1 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India

2 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India

3 UG-CSE & Holy Cross Engineering College, Thoothukudi , India

Abstract Bio-floc technology (BFT) has emerged as a water-efficient and biosecure alternative to conventional pond-based shrimp and fish culture, relying on the in-situ conversion of nitrogenous waste into microbial protein through controlled carbon-to-nitrogen (C:N) ratio management. However, the productivity and stability of bio-floc systems depend on the continuous maintenance of narrow water quality bands for dissolved oxygen, pH, un-ionized ammonia, nitrite, alkalinity, and floc volume, a task that is difficult to sustain through manual testing alone, particularly in small and medium-scale coastal farms. This paper presents an IoT-Based Bio-Floc Water Quality Optimization System (IoT-BWQOS) that combines a low-power wireless sensor network, edge-level anomaly filtering, a cloud-hosted Long Short-Term Memory (LSTM) forecasting model, and an automated actuation layer for aerators, paddle-wheels, and probiotic/carbon dosing pumps. The system was piloted on twelve bio-floc tanks (each of 30 m³ capacity) rearing Pacific white shrimp (Litopenaeus vannamei) at a coastal farm cluster in Nagapattinam district, Tamil Nadu, over a single 110-day culture cycle. Field results indicate a water quality parameter forecasting accuracy of 94.6%, an ammonia-spike early warning lead time of 4–9 hours, a 27% reduction in freshwater/make-up water usage, an 18% improvement in feed conversion ratio, and a 12.4% increase in shrimp survival rate compared to the farm's conventional manual-testing baseline from the preceding culture cycle.

Keywords — Internet of Things, Bio-Floc Technology, Water Quality Monitoring, Aquaculture 4.0, LSTM, LoRaWAN, Shrimp Farming, Smart Sensors, Precision Aquaculture, Edge Computing.