Fish Vista : Comprehensive Intelligent Fish Farming Assistant and Automation
Is an innovative IoT- and AI-based system developed to transform traditional aquaculture into a smart, efficient, and sustainable process. Conventional fish farming often depends on manual monitoring and human experience, resulting in inefficiencies, higher mortality rates, and resource wastage. FISH VISTA overcomes these challenges through real-time data collection, machine learning, and automation. The system employs IoT sensors to monitor key parameters such as temperature, pH, dissolved oxygen, and ammonia levels, transmitting data to a cloud-based platform for AI-driven analysis. This enables early disease detection, anomaly identification, and optimized feeding schedules. Deep learning–based computer vision techniques analyse fish behaviour and estimate growth and biomass. Automation modules control feeding, aeration, and water filtration through adaptive feedback loops, enhancing operational precision. A user-friendly dashboard allows remote monitoring and management. Edge computing minimizes latency, while cloud integration supports large-scale data analytics and predictive insights. Designed for scalability, FISH VISTA benefits both small and large aquaculture setups by reducing feed waste, improving water quality, and increasing productivity. Overall, the system empowers farmers with intelligent decision-making tools and promotes sustainable fish farming practices that balance technological advancement with environmental responsibility
Authors
- Daniel Vilas (ORCID: https://orcid.org/0000-0002-2099-8280)
- B. Sunil (ORCID: https://orcid.org/0000-0002-7769-2856)
- Prof. Gadge S. S
- Waman Mayuri Devidas
Institutions
- John D. and Catherine T. MacArthur Foundation (US)
Publication Details
- Journal
- International Journal of Advanced Research in Science Communication and Technology
- Published
- 2025-11-09
- DOI
- https://doi.org/10.48175/ijarsct-29718
- Primary Topic
- Water Quality Monitoring Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00