Virtual Booth

This project develops an AI-powered monitoring system for aquaculture that integrates real-time fish counting, plant health analysis, and water quality assessment through an interactive web dashboard. The system combines YOLOv5 models for fish detection, MobileNetV2 for water quality classification based on color analysis, and IoT sensors for environmental monitoring, all deployed on Raspberry Pi hardware with AWS cloud infrastructure. Testing achieved 95% accuracy in fish counting, 92% in plant health classification, and 86% in water quality assessment. The integrated solution provides stakeholders with actionable insights for sustainable aquaculture management, addressing traditional limitations of manual monitoring methods while supporting precision farming practices and environmental conservation through automated, data-driven decision-making capabilities.