Smart beekeeping:
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Date
2026
Authors
Journal Title
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Publisher
ACM Digital Library
Abstract
This 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.
Description
Keywords
Beekeeping, Smart beekeeping, Apiculture, Honey bees, Beehive location, Apiary location, Machine learning, Artificial intelligence, Geographic information systems, Spatial analysis, Hive health, Precision agriculture, Pollinator conservation
Citation
Kupsch, S. (2026). Smart beekeeping: Machine learning for healthy hive location analysis. ACM Digital Library, 47-52. https://doi.org/10.1145/3803833.380384
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