[PDF](/assets/research/papers/pdfs/Transforming Beekeeping Through Technology A Systematic Review of Precision Beekeeping.pdf)
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Abstract
Beekeeping is a profitable and mind-relaxing practice; however, monitoring beehives poses significant challenges, such as consuming time and potentially disturbing hive equilibrium, which may lead to colony collapse. Developing precision beekeeping (PB) systems is crucial to assist beekeepers in decision-making, automate redundant hive maintenance, and enhance the security and comfort of bee life. This review systematically explores research on PB systems, based on a keyword-driven search of Scopus and Web of Science databases, yielding 46 relevant publications. The analysis highlights a notable increase in research activity in the field since 2016. The integration of advanced technologies, including machine learning, cloud computing, IoT, and scenario-based communication methods, has proven instrumental in predicting hive states such as queen status, enemy attacks, readiness for harvest, swarming events, and population decline. Commonly measured parameters include hive weight, temperature, and relative humidity, with various sensors employed to ensure precision while minimizing bee disturbance. Additionally, bee traffic monitoring has emerged as a critical approach to assessing hive health. Most studies focus on honeybees rather than stingless bees and, in the context of enemy identification, Varroa destructor is the primary target. This review underscores the potential of novel technologies to revolutionize apiculture and enhance hive management practices.
Relevancy to Gratheon
This review is directly useful as a current map of precision beekeeping systems and product requirements. It consolidates the core monitoring variables Gratheon already targets—hive weight, temperature, humidity, bee traffic, queen status, swarming, enemy attacks, Varroa, and population decline—and places them in a system architecture context that includes IoT, cloud computing, and machine-learning-based decision support. The 46-paper corpus is also a candidate-mining source for future collection updates and competitive landscape work.