Article Details

Abstract

To address issues in traditional fish-vegetable symbiosis systems, such as reliance on artificial experience for feeding, severe feed waste, susceptibility to water quality deterioration, and poor coordination between feeding and ecological cycles, an intelligent and precise feeding management system for fish-vegetable symbiosis has been designed and implemented, using Python as the core development language and based on the YOLOv8 Object Detection algorithm. The system integrates computer vision, Internet of Things, and intelligent control technologies. It utilizes the YOLOv8 model to identify fish density, individual size, and feeding status in real time, and combines the water quality sensor data to construct a dynamic feeding decision-making mechanism, achieving precise control over feed quantity and feeding timing. A five-layer architecture design, including the presentation layer, application layer, domain layer, data collection layer, and infrastructure layer, has been adopted to complete hardware integration and software development, enabling automation of the feeding process, real-time status monitoring, and intelligent decision-making and control. The experimental results indicate that the system achieves a feeding accuracy of ±5 grams, a response time of ≤3 seconds, a fish behavior recognition accuracy of 91.8%, a 5% to 10% improvement in fish survival rates, and a 15% to 20% increase in feed utilization efficiency. This system provides technical support for the intelligent upgrading of fish-vegetable symbiosis models, demonstrating strong practicality and promotional value.

Keywords

fish-vegetable symbiosis; YOLOv8 algorithm; intelligent management; precision feeding

References

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Funding

2025 National College Student Innovation Training Program Project (202516205025); 2025 Guangxi Agricultural Vocational and Technical University School level Teaching and Research Reform Project (XJG2573)

About the authors

Tian Jiacheng (November 2004-), male, Han ethnicity, from Dezhou, Shandong Province, is currently pursuing a bachelor's degree. His research interests include artificial intelligence technology and computer applications; Corresponding author: He Linhong (1994-), female, Han ethnicity, from Heng County, Guangxi. Lecturer, undergraduate, research direction: artificial intelligence technology, computer applications, agricultural informatization, higher vocational education.