[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121157-en":3,"doc-seo-121157-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121157,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Optimizing Feeding Strategies in Aquaculture Using Machine Learning - Ensuring Sustainable and Economically Viable Fish Farming Practices","Aquaculture faces critical needs for feeding strategies that improve fish growth while reducing environmental impact and maintaining economic viability. Conventional methods often fail to adapt to changing environmental conditions and fish growth rates, causing suboptimal growth, waste, and ecological degradation. This study presents a machine learning framework that predicts optimal feeding rates from real-time sensor, video, and logging data, targeting SGR, RGI, and FCR while minimizing feed spillage. Results show improved performance over traditional approaches.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia Computer Science 246 (2024) 4712–4721  \n28th International Conference on Knowledge-Based and Intelligent Information & Engineering  \nSystems (KES 2024)  \nOptimizing Feeding Strategies in Aquaculture Using Machine Learning: Ensuring Sustainable and Economically Viable Fish  \nFarming Practices  \nAya Saada,∗, Alexia Baikasa , Mette Remena , Finn Olav Bjørnsona  \na Aquaculture Department, SINTEF Ocean AS, Trondheim, Norway  \nAbstract  \nThe 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.  \n© 2024 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems  \nKeywords: Knowledge representation; graph database; reasoning strategies; Robust AI; process optimization, fish farm feeding process; aquaculture  \n∗ Corresponding author.  \nE-mail address: [aya.saad@sintef.no](aya.saad@sintef.no)  \n1877-0509 © 2024 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems  \n10.1016/j.procs.2024.09.336  \nAya Saad et al. / Procedia Computer Science 246 (2024) 4712–4721 4713  \n1. Introduction  \nThe aquaculture industry faces unique challenges and opportunities in sustainably managing global food resources. As the global population continues to grow and the demand for protein-rich diets increases, aquaculture has become an essential part of food production. However, the sustainability of aquaculture practices, especially feeding strategies, is vital to ensuring environmental, economic, and social sustainability. The Precision Fish Farming (PFF) [8] introducesa framework aimed at optimizing operations within the aquaculture industry through a cycle of data-driven actions: 1 . Observe (collect data)","cbCaidglsh4rXux4","https://ap.wps.com/l/cbCaidglsh4rXux4","pdf",1500661,1,10,"English","en",105,"# Introduction\n## Precision Fish Farming feeding cycle\n## Limitations of traditional static schedules\n## Purpose and machine learning framework overview\n# (Paper continues) Data collection, model training, selection, and validation","[{\"question\":\"Why do traditional aquaculture feeding methods often underperform?\",\"answer\":\"They typically rely on static feeding schedules that do not adapt to changing environmental conditions or varying fish growth rates, which can cause overfeeding or underfeeding.\"},{\"question\":\"What data sources does the machine learning framework use to predict feeding rates?\",\"answer\":\"It integrates real-time environmental sensor data, video analytics, and manual logging to estimate optimal feed amounts dynamically.\"},{\"question\":\"Which growth and efficiency indicators does the framework aim to optimize?\",\"answer\":\"The approach targets Specific Growth Rate (SGR), Relative Growth Index (RGI), and Feed Conversion Ratio (FCR) while minimizing feed spillage.\"}]","Optimizing Feeding Strategies in Aquaculture Using Machine Learning - 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