Abstract
This paper presents a bee-condition-monitoring system incorporated with a deep-learning process to detect bee swarming. This system includes easy-to-use image acquisition and various end node approaches for either on-site or cloud-based mechanisms. This system also incorporates a new smart CNN engine called Swarm-engine for detecting bees and the issue of notifications in cases of bee swarming conditions to the apiarists. First, this paper presents the authors’ proposed implementation system architecture and end node versions that put it to the test. Then, several pre-trained networks of the authors’ proposed CNN Swarm-engine were also validated to detect bee-clustering events that may lead to swarming. Finally, their accuracy and performance towards detection were evaluated using both cloud cores and embedded ARM devices on parts of the system’s different end-node implementations.
Highlights
The Internet of Things (IoT) industry is shifting fast towards the agricultural sector, aiming for the vast applicability of new technologies
Several bee-monitoring and beekeepingresource management systems or frameworks that incorporate IoT and smart services have been proposed in the literature [5,6,7], while others exist as market solutions
This paper investigates existing technological systems focusing on detecting bee stress, queen succession, or Colony Collapse Disorder (CCD), favorable conditions that can lead to bee swarming
Summary
The Internet of Things (IoT) industry is shifting fast towards the agricultural sector, aiming for the vast applicability of new technologies. An essential factor in apiary hives that affects both colony survival and honey yield is the ability to manage agricultural interventions and disease treatments (especially Varroa mite [8]) and monitor the conditions inside the beehive [9,10,11]. High humidity levels can lead to swarming events inside the beehive, excessive colony honey consumption, and the production of propolis and wax by the bees as a countermeasure [19,20]. Since swarming events occur mainly during the day and in the spring and summer months, as indicated by apiarists, this paper presents a new camera sensor system for detecting swarming inside the beehive. The paper summarizes the findings and experimental results of the system
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