Author(s) :

Su-Yi Yu.

Volume/Issue :

Abstract :

Large yellow croaker (Larimichthys crocea) aquaculture represents a critical component of China's marine economy, with Ningde City serving as the nation's largest production center. However, the industry faces substantial challenges related to water quality management, with traditional manual monitoring approaches proving inadequate for early detection of environmental stressors. The paper developed and validated an integrated Internet of Things (IoT)-based water quality monitoring and early warning system specifically optimized for large yellow croaker aquaculture operations. The system employed LoRaWAN communication protocol for long-range data transmission(up to 15 km), multi-parameter sensors monitoring dissolved oxygen, temperature, pH, salinity, ammonia nitrogen, and turbidity at 15-minute intervals, and machine learning algorithms (Random Forest) for predictive analytics. Field trials conducted across 12 commercial farms in Ningde's coastal waters (January 2023-June 2024) demonstrated high system reliability(97.2% uptime) and strong predictive performance (89.3% accuracy for 6-hour ahead warnings). IoT-equipped farms achieved significant improvements compared to control farms: 42.6% reduction in mortality rate(8.4% vs 14.6%), 18.2% improvement in feed conversion ratio(1.35 vs 1.65), 14.7% increase in average harvest weight(478g vs 417g), and 31.5% reduction in labor requirements. Economic analysis revealed favorable return on investment (287% over two production cycles) with payback period of 6.4 months. These results demonstrate that precision aquaculture technologies can deliver substantial operational improvements while enhancing environmental stewardship. The technical framework offers a scalable architecture adaptable to other aquaculture species and geographical contexts, with implications for climate change adaptation and sustainable intensification of marine food production.

Keywords :

Internet of things, Water quality monitoring, Large yellow croaker, Aquaculture, Early warning system, Precision farming, LoRaWAN, Machine learning

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