Precision Aqua-Farming: Anticipating Fluctuating Weights of Livestock in Aquaculture using Machine Learning and IoT

Precision aqua farming has emerged as a crucial paradigm in modern aqua-agriculture, enabling optimized resource allocation and enhanced productivity. In the context of aquaculture, where the accurate estimation of livestock weight is vital for sustainable and efficient operations, the application of precision aqua-farming principles becomes even more critical. A comprehensive study is demanded on predicting fluctuating livestock weights in aquaculture through advanced data-driven techniques, and IoT usage. Traditional methods of weight estimation in aquaculture have often led to imprecise results due to the dynamic nature of aquatic environments and the complex interplay of various factors affecting livestock growth. In contrast, the proposed aqua-precision farming approach leverages cutting-edge technologies such as the usage of IoT, machine learning, and artificial intelligence to analyze vast datasets encompassing environmental parameters, feed composition, health records, and historical weight data. By continuously monitoring and analyzing real-time data, the system can anticipate fluctuations in livestock weights, aiding aquaculture farmers in making proactive and well-informed decisions. The proposed AI and IoT-based approach offers scalability and adaptability, making it suitable for various aquaculture settings, from small-scale inland ponds to large offshore facilities. As the repository of environmental and weight data grows, the AI models can continuously improve their predictive accuracy, enhancing the overall effectiveness of the approach.