000			03843nam a22003377a 4500
003			OSt
005			20250710164427.0
008			250710b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.9 _bC36 2025
100			_aCanto, Trishalyn B. _eauthor
245			_aDevelopment of iot monitoring system for pig pens/ _cTrishalyn B. Canto, Angelo James L. Forbes, Stephen Gabriel T. Onahon, Carlos Alfonzo R. Rumbaoa, Jay Mark M. Santos, and Leoniel R. Valenzuela.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_axiv, 132pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Industrial Technology.-- _bBachelor of engineering technology major in computer engineering technology: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aSmall-scale piggery operations, backyard pigpens, face significant challenges including too much labor work, poor waste management, and inconsistent feeding practices that impact animal welfare and operational efficiency. Traditional backyard farming environments often lack proper monitoring systems, leading to not ideal conditions of pigpen and increased health risks for livestock. With this, the researchers intended to address these limitations by developing IoT Monitoring for pig pens. An integrated monitoring system tailored specifically for small-scale piggery operations like the backyard piggery farms, which combines real-time environmental monitoring, automated farm management, and remote supervision capabilities. The system integrates MQ-135 gas sensors for ammonia level detection and DHT11 sensors for temperature and humidity tracking, along with ESP32-CAM modules for live video streaming and a comprehensive mobile application interface. The prototype was developed using the Agile Scrum methodology with iterative sprints, daily stand-ups, and continuous feedback integration to ensure alignment with user requirements. The researchers conducted extensive testing including unit tests, integration assessments, performance evaluations, user acceptance testing with farmers, and on-site trials in actual pigpen environments. Results demonstrate that the system adheres to ISO 25010 software quality standards with strong performance across key metrics, particularly, functionality, accuracy, and reliability. The MQ-135 gas sensor showed an average percentage error of 2.88% compared to reference ammonia levels, while the DHT11 resulted an average percentage error of 2.10% for temperature and 1.83% for humidity, confirming high sensor reliability. Software testing using a structured test matrix for core features such as user login, sensor display, and automated controls yielded an average pass rate of 100%, verifying expected behavior and functional correctness. The mobile application provides real-time sensor readings, manual controls, automation features for cleaning and feeding, and alert notifications when environmental thresholds are exceeded. This innovation contributes to enhanced animal welfare, improved operational efficiency, and sustainable pig pen management through intelligent IoT connectivity and remote monitoring capabilities, aligning with modern agricultural technology standards and practical farming needs.
650			_aDHT 11
650			_aPigPen IoT
650			_aISO 25010
700			_aForbes, Angelo James L. _eauthor
700			_aOnahon, Stephen Gabriel T. _eauthor
700			_aRumbaoa, Carlos Alfonzo R. _eauthor
700			_aSantos, Jay Mark M. _eauthor
700			_aValenzuela, Leoniel R. _eauthor
942			_2lcc _cBTH CIT _n0
999			_c30274 _d30274