AI-Optimised Model for Resource Management in Aquaculture-Agriculture Systems

An AI-optimized model for resource management in integrated aquaculture-agriculture (IAA) systems is the focus of this study. The main goal of this research is to find out how AI can optimize the use of water, nutrients, and energy in aquaculture and agriculture to make these industries more efficient, sustainable, and productive. The model draws on machine learning techniques to incorporate up-to-the-minute information from aquaculture and agricultural settings, such as soil moisture, nutrient levels, weather, and fish biomass. The results demonstrate that AI-driven insights and predictive skills can significantly boost crop and fish yields while using significantly less resources. The study demonstrates how AI can assist in sustainable IAA practices, offering recommendations for improving the models and conducting field testing to facilitate their scaling to various locations.