Smart IoT-based visual detection system for aquaculture monitoring using YOLO and edge computing
Year: 2025
Type: article
Abstract: Aquaculture is crucial for global food security, offering sustainable protein amid environmental challenges and declining wild stocks. This study develops a smart IoT-enabled real-time monitoring system for aquaculture farms, focused on detecting and counting post-larval redclaw crayfish (Cherax quadricarinatus). The system integrates a Raspberry Pi 4 Model B with a high-resolution camera and the YOLOv5s deep learning model, performing local image processing through edge computing to reduce latency and network load. A factorial experimental design evaluated system performance across four groups varying by molting status (molted vs. non-molted) and environment (covered vs. open-air ponds). Results showed the highest detection accuracy in non-molted crayfish under open-air conditions (F1-score = 0.93), with precision, recall, and [email protected] exceeding 90%, while molted crayfish in covered ponds had the lowest scores (F1-score = 0.85). Statistical analyses confirmed significant effects of both molting and lighting on detection performance and their interaction (p < 0.05). Robustness tests demonstrated model stability under noise and variable lighting, with F1-scores remaining above 0.80. The system provides a scalable, cost-effective solution that improves operational efficiency, reduces manual labor, and supports sustainable aquaculture by delivering timely alerts for abnormal crayfish behavior, enabling proactive farm management.
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4414335275
FWCI:
Cites:
Cited by:
Related to: