Beemo 2.0: raspberry pi-based beehive status monitoring system for sustainable beekeeping practice utilizing queen bee detection through tooting or buzzing using cnn audio analysis/ Christian Justine O. Bascos, Azphyr B. Biñar, Mark Edren G. Dela Cruz, Guile F. Fiedlan, Kyla Trisha B. Toliao, and Carl Joseph D. Torres.--

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Bascos, Christian Justine O [author]

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TextPublication details: Manila: Technological University of the Philippines, 2025.Description: xv, 218pages: 29cmContent type:

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BTH TK 870 B37 2025

Dissertation note: College Of Engineering.-- Bachelor of science in electronics engineering: Technological University of the Philippines, 2025. Summary: The decline of bees is a major threat to biodiversity, agriculture and food security. Honeybees are effective pollinators, and in the center of every colony is a thriving queen bee. If the queen is missing or weakened, the colony may collapse, so its monitoring is important for beekeeping sustainability. Yet conventional hive monitoring approaches can be invasive, time-consuming and disturb the colony. In response, BEEMO 2.0 is introduced as a bee monitoring system implemented on the Raspberry Pi allowing the surveillance of beehive and based on the detection and processing of the buzzing or tooting sound emitted by the queen bee using CNN audio analysis. As a result of the sound-based beehive monitoring, BEEMO 2.0 helps non-invasively detect the presence of queen instantly while eliminating risk due to human manual hive inspection. The system also has built-in environmental sensors to track temperature and humidity as well, offering beekeepers essential hive health information. This data is communicated via an IoT- enabled mobile app for remote monitoring and timely action. The use of CNNs for audio classification leads to precise identification of hive-related important states, so that preventive monitoring of the colony state can be made. The BEEMO 2.0 will provide a new level of precision for hive monitoring, result in a higher rate of bee colony survival, and help the beekeeping industry become more sustainable. The research effectively created BEEMO 2.0 using different machine-learning algorithms, particularly CNN, as the system had a high tolerance level in classifying audio signals, a feature crucial in managing a hive. And thus, by adding the temperature and humidity sensors, it kept track of the overall data of the condition of the hive more accurately.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TK 870 B37 2025 (Browse shelf(Opens below))	c.1	Not for loan		BTH0006463

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Bachelor's thesis

College Of Engineering.-- Bachelor of science in electronics engineering: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

The decline of bees is a major threat to biodiversity, agriculture and food security.
Honeybees are effective pollinators, and in the center of every colony is a thriving queen
bee. If the queen is missing or weakened, the colony may collapse, so its monitoring is
important for beekeeping sustainability. Yet conventional hive monitoring approaches can
be invasive, time-consuming and disturb the colony. In response, BEEMO 2.0 is
introduced as a bee monitoring system implemented on the Raspberry Pi allowing the
surveillance of beehive and based on the detection and processing of the buzzing or tooting
sound emitted by the queen bee using CNN audio analysis. As a result of the sound-based
beehive monitoring, BEEMO 2.0 helps non-invasively detect the presence of queen
instantly while eliminating risk due to human manual hive inspection. The system also has
built-in environmental sensors to track temperature and humidity as well, offering

beekeepers essential hive health information. This data is communicated via an IoT-
enabled mobile app for remote monitoring and timely action. The use of CNNs for audio

classification leads to precise identification of hive-related important states, so that
preventive monitoring of the colony state can be made. The BEEMO 2.0 will provide a
new level of precision for hive monitoring, result in a higher rate of bee colony survival,
and help the beekeeping industry become more sustainable. The research effectively
created BEEMO 2.0 using different machine-learning algorithms, particularly CNN, as the
system had a high tolerance level in classifying audio signals, a feature crucial in
managing a hive. And thus, by adding the temperature and humidity sensors, it kept track
of the overall data of the condition of the hive more accurately.

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