Implementación de AquaNutri AI Móvil: Herramienta de Apoyo Inteligente para la Toma de Decisiones y la Mejora de la Productividad en Sistemas Acuícolas Biofloc y RAS en Colombia

Abstract

Intensive aquaculture in Colombia, especially under Biofloc Technology (BFT) and Recirculating Aquaculture Systems (RAS), faces limitations in water quality management due to reliance on manual measurements and reactive decisions that increase the risk of mortality, low feed efficiency, and high production costs. The aim is to develop and implement the AquaNutri AI Mobile platform as an intelligent support tool for monitoring, prediction, and control of critical physicochemical parameters, to improve the productivity and sustainability of aquaculture systems. A quantitative, applied, and experimental approach was adopted, structured in four phases: design of a mobile interface, implementation of an automatic alert system, integration of correlational and predictive analysis models through artificial intelligence, and comprehensive field validation with measurements of mortality, energy consumption, feed conversion ratio (FCR), and user perception using the Likert scale. The prototype to be developed will enable real-time data capture, issue preventive alerts, and generate useful predictions for decision-making, projecting improvements of 10–20% in survival, an 8–10% reduction in energy consumption, and a 5–8% optimization of FCR. The findings demonstrate that AquaNutri AI Mobile will contribute to transforming traditional aquaculture into a data-driven process, democratizing access to technology for small and medium producers, aligning with the National Plan for the Development of Sustainable Aquaculture (PlaNDAS) and CONPES 4144 of 2025, and consolidating itself as a viable and scalable proposal towards smart aquaculture in Colombia.