000			03050nam a22003137a 4500
003			OSt
005			20260616102409.0
008			260616b |||||||| |||| 00| 0 eng d
040			_bEnglish _cTUPM _dTUPM _erda
050			_aBTH TK 5105.59 _bD86 2025
100			_aDumapit, Joanna Mariel T. _eAuthor
245			_aDevelopment of IOT-Based Health Monitoring System for Swine/ _cJoanna Mariel T. Dumapit, Miguel P. Odones, Rosel M. Roda, and Johnelle Irish T. Sapungan..-
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_avii, 135 pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's Thesis
502			_aCollege of Industrial Technology..- _bBachelor of Engineering Technology Major in Electronic Communications Technology: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aSwine farming is a cornerstone of global food security, yet remains highly vulnerable to rapid disease outbreaks like African Swine Fever (ASF). Traditional health checks rely on manual observation, which is often slow, labor-intensive, and prone to missing early red flags such as lethargy or appetite loss. The rationale for this study stems from the immense economic burden of these diseases; for instance, ASF caused an estimated PHP 50 billion loss in the Philippines, highlighting the inadequacy of existing diagnostic tools for smallholder farms. Consequently, the objective of the study was to develop an IoT-based Swine Monitoring System for early symptom detection to assist resource-limited farmers. The methodology involved fabricating a waterproof, 3D-printed prototype integrated with an ESP32 microcontroller and a comprehensive suite of sensors. The system utilized an AMG8833 IR thermal camera for non-contact body temperature monitoring, an HTU21D sensor for environmental temperature and humidity, and PIR and ultrasonic sensors to track animal movement and feeding behavior. Data was transmitted via MQTT protocol to a dedicated cloud platform called "Babekare," enabling real-time monitoring and immediate alerts. Experimental results demonstrated high precision, with average percentage errors of 1.50% for thermal readings, 1.60% for motion detection, and 0.28% for food intake monitoring. While reliability remains sensitive to network stability and sensor calibration, the system proved effective in identifying early physiological signs of illness. This IoT solution promotes sustainable livestock practices, reduces economic losses, and improves overall herd health outcomes. Keywords: Internet of Things (IoT), Swine Health Monitoring, Thermal Sensor
650			_aElectronic Communications Technology
650			_aInternet of Things (IoT)
650			_aSwine Health Monitoring
700			_aOdones, Miguel P. _eAuthor
700			_aRoda, Rosel M. _eAuthor
700			_aSapungan, Johnelle Irish T. _eAuthor
942			_2lcc _cBTH CIT _n0
999			_c31532 _d31531