Smart beekeeping:

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creativework.keywordssmart farming, apiculture, machine learning, computer vision, droneimagery, lightweight web application, software quality evaluation,ISO/IEC 25010
dc.contributor.authorKupsch, Stephan
dc.date.accessioned2026-08-10T02:16:18Z
dc.date.available2026-08-10T02:16:18Z
dc.date.issued2026
dc.description.abstractThis paper presents HaBEEtat WebApp, machine learning-driven application designed to enhance decision-making in apiculture by automating the identification of suitable hive locations. Utilizing drone-captured imagery and lightweight scripting through Tracking.js (a 7KB JavaScript computer vision library), the system analyzes environmental features such as sunlight exposure, vegetation density, and windbreaks to determine areas conducive to healthy hive development. The web-based platform embodies the principles of smart farming and precision agriculture by integrating image processing, artificial intelligence, and geospatial analysis for practical field applications. The performance and usability of HaBEEtat WebApp were evaluated using ISO/IEC 25010 software quality model, covering eight key quality attributes: functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. Results revealed high acceptability, with median ratings ranging from 4.0 (Agree) to 5.0 (Strongly Agree) across most categories, demonstrating the web app's reliability, interoperability, and efficiency in real-world agricultural settings. Findings confirm that lightweight machine learning architectures can effectively support small-scale and remote beekeepers, providing a low-resource yet powerful decision-support tool. The study concludes that HaBEEtat WebApp contributes to sustainable apiculture practices, aligns with digital transformation initiatives in agriculture, and offers a replicable model for other smart farming applications. Future work recommends developing a mobile-integrated version for real-time drone-assisted operations and wider accessibility among local apiculturists.
dc.identifier.citationKupsch, S. (2026). Smart beekeeping: Machine learning for healthy hive location analysis. ACM Digital Library, 47-52. https://doi.org/10.1145/3803833.380384
dc.identifier.isbn979-8-4007-2209-7/26/02
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2298
dc.language.isoen
dc.publisherACM Digital Library
dc.relation.urihttps://dl.acm.org/doi/10.1145/3803833.3803841
dc.relation.urihttps://dl.acm.org/doi/epdf/10.1145/3803833.3803841
dc.rights.licenseCC BY 4.0
dc.sdgSDG 2
dc.subjectBeekeeping
dc.subjectSmart beekeeping
dc.subjectApiculture
dc.subjectHoney bees
dc.subjectBeehive location
dc.subjectApiary location
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.subjectGeographic information systems
dc.subjectSpatial analysis
dc.subjectHive health
dc.subjectPrecision agriculture
dc.subjectPollinator conservation
dc.subject.ddcBeekeeping
dc.subject.ddcArtificial intelligence / Machine learning
dc.subject.lcshBee culture
dc.subject.lcshHoneybee culture
dc.subject.lcshBeehives
dc.subject.lcshHoneybees--Habitat
dc.subject.lcshMachine learning
dc.subject.lcshArtificial intelligence
dc.subject.lcshGeographic information systems
dc.subject.lcshGeographic information systems
dc.subject.lcshGeographic information systems
dc.subject.lcshAgriculture--Data processing
dc.subject.lcshPollinators--Conservation
dc.titleSmart beekeeping:
dc.title.alternativeMachine learning for healthy hive location analysis
dc.typeArticle
local.subject.scientificnameApis mellifera L.
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