Development of IOT-Based Health Monitoring System for Swine/ Joanna Mariel T. Dumapit, Miguel P. Odones, Rosel M. Roda, and Johnelle Irish T. Sapungan..-

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Dumapit, Joanna Mariel T [Author]

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TextPublication details: Manila: Technological University of the Philippines, 2025.Description: vii, 135 pages: 29cmContent type:

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BTH TK 5105.59 D86 2025

Dissertation note: College of Industrial Technology..- Bachelor of Engineering Technology Major in Electronic Communications Technology: Technological University of the Philippines, 2025. Summary: Swine 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

List(s) this item appears in: CIT-BT-Electronic Communications Engineering Technology

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Item type	Current library	Shelving location	Call number	Status	Date due	Barcode
Bachelor's Thesis CIT	TUP Manila Library	Thesis Section-2nd floor	BTH TK 5105.59 D86 2025 (Browse shelf(Opens below))	Not for loan		BTH0006962

Bachelor's Thesis

College of Industrial Technology..- Bachelor of Engineering Technology Major in Electronic Communications Technology: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

Swine 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

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