Development of iot monitoring system for pig pens/ Trishalyn B. Canto, Angelo James L. Forbes, Stephen Gabriel T. Onahon, Carlos Alfonzo R. Rumbaoa, Jay Mark M. Santos, and Leoniel R. Valenzuela.--

Bachelor's thesis

College of Industrial Technology.--
Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

Small-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.