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Abstract
Precision beekeeping has emerged as a response to the need for optimizing beekeeping management using sensors and specialized programs, aiming to provide beekeepers with insights into hive conditions without the need for frequent inspections. While the field has made solid advances, particularly with data such as hive weight and temperature, recent years have seen the emergence of sound and image-based methods. The review identifies unmet needs for sensors capable of detecting queen bee presence, Varroa mite levels, disease and predator presence, and swarming behaviour, along with improved prediction of honey harvest timing and feeding requirements. It argues that integrating data collection, machine learning, artificial intelligence, and best-management practices into intelligent apiary-management systems is crucial for the advancement of precision beekeeping.
Relevancy to Gratheon
This review is valuable for Gratheon's product planning because it maps sensor modalities to the practical service layer beekeepers need: queen-state confirmation, Varroa and disease alerts, swarming prediction, feeding/honey-harvest timing, and integrated decision support. It reinforces that a useful monitoring product is not just a sensor box but a service that fuses field data, AI, and best-practice guidance. The paper can guide Gratheon's roadmap prioritization and help compare proposed features against the current state of precision-beekeeping services.