Cabanela, Joseph Laurence L <br/><br/>
Development of Water Monitoring System with weight  Segregation for EEL Aquaculture 
Joseph Laurence L. Cabanela, Genghiel Eros P. Castillo, Paolo D. Monteron and Evol Adrian Peralta..- 
 - Manila:   Technological University of the Philippines   2025 
 - ix, 80pages:  29cm. 
<br/><br/> Bachelor's Thesis  
<br/><br/>College of Industrial Technology..- 
<br/><br/>Includes bibliographic references and index. 
<br/><br/> Eel aquaculture is an growing sector in aquaculture especially in East Asian countries like<br/>China, Japan, and Korea that requires reliable monitoring and standardized handling<br/><br/>systems to ensure productivity and larger profit margins. The lack of consistent weight-<br/>based segregation and reliable continuous water quality monitoring continues to be the<br/><br/>main problems faced by farmers in this sector, this is caused by old fashioned measurement<br/>practices and unstable live weighing conditions. Recent innovations in research have<br/>developed automated water monitoring and fish measurement devices, showing how<br/>sensor-based and machine-assisted approaches can improve aquaculture management. But<br/>in the current shape of the industry, most current solutions focus on monitoring or sorting<br/>only, with limited integration of real-time water parameter sensing and automated eel<br/>weight-based segregation within a single system. This study aims to design and implement<br/>an IoT-based prototype capable of water quality monitoring and automated eel sizing and<br/>segregation. A prototype-based development methodology was used in this capstone study,<br/>integrating pH, temperature, total dissolved solids, and turbidity sensors with a load-cell<br/>and automated sorting mechanism, followed by paired statistical validation tests against<br/>controlled variables. Test results showed a mean weighing deviation of 1.13 grams with<br/>statistically detectable but insignificant error, while pH, temperature, and TDS sensors<br/>showed no significant difference from reference measurements and turbidity showed a<br/>small significant difference, with automated sorting completed in approximately 9–11<br/>seconds per eel. The system provides reliable water monitoring system that can greatly<br/>help farmers with providing the eels the best environment they can reside in, alongside an<br/>automated sorting system that can streamline the workflow of weighing eels when<br/>separating them into their different weight classes.<br/>Keywords: eel aquaculture, water quality monitoring, eel segregation, IoT monitoring,<br/>weight-based sorting 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Electronics Engineering Technology<br/>eel aquaculture<br/>water quality monitoring
<br/><br/><label>LC Class. No.: </label>BTH TK 870   / C33 2025