[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120848-en":3,"doc-seo-120848-105":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},120848,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","IoT-Based Environmental Control System for Fish Farms - Decision Support","IoT-based environmental control is proposed for fish farming to meet rising seafood demand and to address the difficulty of maintaining stable farm conditions. A wireless sensor network gathers real-time water temperature, pH, humidity, and fish behavior, followed by preprocessing such as imputation, outlier detection, feature engineering, and synchronization. Four machine learning models provide decision support: Random Forests optimize temperature and pH, SVMs issue early disease and parasite warnings, GBMs adapt feeding schedules for efficiency, and neural networks control pumps and heaters. Combined decisions align conditions with specifications, improving fish health and productivity while reducing resource waste for sustainable profitability.","IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning  \nDecision Support  \nD. Dhinakaran1, S. Gopalakrishnan2, M.D. Manigandan3, T. P. Anish4  \n1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,  \nChennai, India.  \n*[Corresponding Author:drdhinakarand@veltech.edu.in](Corresponding Author:drdhinakarand@veltech.edu.in)  \n2Department of Computer Science & Engineering (Data Science), Madanapalle Institute of Technology & Science,  \nAndhra Pradesh, India.  \n[gopal.pgsk@gmail.com](gopal.pgsk@gmail.com)  \n3Department of Electronics and Communication Engineering,  \nPanimalar Engineering College,  \nChennai, India.  \n[manijow92@gmail.com](manijow92@gmail.com)  \n4Department of Computer Science and Engineering,  \nR.M.K College of Engineering and Technology, Chennai, India.  \n[anishcse@rmkcet.ac.in](anishcse@rmkcet.ac.in)  \nAbstract—In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoTbased environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.  \nKeywords-Fish farming, IoT, support vector machines, environmental factors, gradient boosting machines, water quality, temperature.  \nI. INTRODUCTION  \nFish aquaculture has emerged as a critical sector of the agriculture industry, tasked with meeting the escalating global demand for seafood [1] . While this industry's growth is undeniable, the complexities of managing fish farms require innovative solutions to address the dynamic and multifaceted challenges posed by environmental control. Successful fish farm management hinges on maintaining optimal conditions for aquatic life, with key factors such as water quality,  \ntemperature, and feeding schedules playing pivotal roles. However, the intricacies and interdependencies of these parameters necessitate a sophisticated approach to their management [2-4] . In response to these challenges, we introduce a pioneering Internet of Things (IoT)-based environmental control system that combines sensor integration with advanced machine learning decision support. This system represents a paradigm shift in fish farm management, bridging the gap between data-driven precision and the multifarious  \nrequirements of aquaculture. The integration of a wir","cbCairCIM38PQBqB","https://ap.wps.com/l/cbCairCIM38PQBqB","pdf",373481,1,15,"English","en",105,"# Introduction\n## Background and Significance\n# System Overview\n## Sensor Integration and Data Collection\n## Data Preprocessing Pipeline\n## Machine Learning Decision Support Models","[{\"question\":\"How does the system collect information from a fish farm?\",\"answer\":\"It uses a wireless sensor network inside the farm to continuously collect real-time measurements such as water temperature, pH, humidity, and fish behavior.\"},{\"question\":\"What role do machine learning algorithms play in decision support?\",\"answer\":\"Random Forests predict and optimize temperature and pH, SVMs provide early warnings for diseases and parasites, GBMs adjust feeding schedules dynamically, and neural networks control equipment like pumps and heaters.\"},{\"question\":\"What preprocessing steps are applied to sensor data?\",\"answer\":\"The collected data undergo imputation, outlier detection, feature engineering, and synchronization to improve reliability and produce a robust dataset for analysis.\"}]","IoT-Based Environmental Control System for Fish Farms - 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