Abstract
The aquaculture industry faces critical challenges in optimizing feeding strategies to enhance fish growth while minimizing environmental impacts and ensuring economic viability. Traditional feeding methods often fall short in adapting to dynamic environmental conditions and fish growth rates, leading to suboptimal growth, waste, and environmental degradation.
To address these issues, this study introduces a robust machine learning-based framework designed to optimize feeding processes in aquaculture. The framework employs advanced regression models such as Gradient Boosting Regressor, Elastic Net Regression, and Support Vector Regression to predict optimal feeding rates with high accuracy and efficiency. Our methodology integrates real-time data from environmental sensors, video analytics, and manual logging to predict the optimal feed amount.
The goal of this comprehensive approach is to achieve high growth performance indicators such as Specific Growth Rate (SGR), Relative Growth Index (RGI), and optimal Feed Conversion Ratio (FCR), while also ensuring minimal feed spillage. By employing machine learning, we can dynamically adjust feeding amounts based on fish appetite and environmental conditions, thus ensuring sustainable and economically viable fish farming practices. This paper details the implementation of this framework, encompassing data collection and cataloging, model training, selection, and validation processes, and discusses the significant improvements over traditional methods. Our results demonstrate the model’s effectiveness in reducing waste and enhancing fish growth, illustrating the potential for wider application within the aquaculture industry.
To address these issues, this study introduces a robust machine learning-based framework designed to optimize feeding processes in aquaculture. The framework employs advanced regression models such as Gradient Boosting Regressor, Elastic Net Regression, and Support Vector Regression to predict optimal feeding rates with high accuracy and efficiency. Our methodology integrates real-time data from environmental sensors, video analytics, and manual logging to predict the optimal feed amount.
The goal of this comprehensive approach is to achieve high growth performance indicators such as Specific Growth Rate (SGR), Relative Growth Index (RGI), and optimal Feed Conversion Ratio (FCR), while also ensuring minimal feed spillage. By employing machine learning, we can dynamically adjust feeding amounts based on fish appetite and environmental conditions, thus ensuring sustainable and economically viable fish farming practices. This paper details the implementation of this framework, encompassing data collection and cataloging, model training, selection, and validation processes, and discusses the significant improvements over traditional methods. Our results demonstrate the model’s effectiveness in reducing waste and enhancing fish growth, illustrating the potential for wider application within the aquaculture industry.