000			03385nam a22003137a 4500
003			OSt
005			20260611172812.0
008			260611b |||||||| |||| 00| 0 eng d
040			_bEnglish _cTUPM _dTUPM _erda
050			_aBTH TK 870 _bC33 2025
100			_aCabanela, Joseph Laurence L _eAuthor
245			_aDevelopment of Water Monitoring System with weight Segregation for EEL Aquaculture _cJoseph Laurence L. Cabanela, Genghiel Eros P. Castillo, Paolo D. Monteron and Evol Adrian Peralta..-
260			_aManila: _bTechnological University of the Philippines _c2025
300			_aix, 80pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's Thesis
502			_aCollege of Industrial Technology..- _bBachelor of Engineering Technology Major in Electronics Engineering Technology: _cTechnological University of the Philippines, _d2025
504			_aIncludes bibliographic references and index.
520			_aEel aquaculture is an growing sector in aquaculture especially in East Asian countries like China, Japan, and Korea that requires reliable monitoring and standardized handling systems to ensure productivity and larger profit margins. The lack of consistent weight- based segregation and reliable continuous water quality monitoring continues to be the main problems faced by farmers in this sector, this is caused by old fashioned measurement practices and unstable live weighing conditions. Recent innovations in research have developed automated water monitoring and fish measurement devices, showing how sensor-based and machine-assisted approaches can improve aquaculture management. But in the current shape of the industry, most current solutions focus on monitoring or sorting only, with limited integration of real-time water parameter sensing and automated eel weight-based segregation within a single system. This study aims to design and implement an IoT-based prototype capable of water quality monitoring and automated eel sizing and segregation. A prototype-based development methodology was used in this capstone study, integrating pH, temperature, total dissolved solids, and turbidity sensors with a load-cell and automated sorting mechanism, followed by paired statistical validation tests against controlled variables. Test results showed a mean weighing deviation of 1.13 grams with statistically detectable but insignificant error, while pH, temperature, and TDS sensors showed no significant difference from reference measurements and turbidity showed a small significant difference, with automated sorting completed in approximately 9–11 seconds per eel. The system provides reliable water monitoring system that can greatly help farmers with providing the eels the best environment they can reside in, alongside an automated sorting system that can streamline the workflow of weighing eels when separating them into their different weight classes. Keywords: eel aquaculture, water quality monitoring, eel segregation, IoT monitoring, weight-based sorting
650			_aElectronics Engineering Technology
650			_aeel aquaculture
650			_awater quality monitoring
700			_aCastillo, Genghiel Eros P. _eAuthor
700			_aMonteron, Paolo D. _eAuthor
700			_aPeralta, Evol Adrian _eAuthor
942			_2lcc _cBTH CIT _n0
999			_c31509 _d31508