Method for Recognizing Behavioral Variation Within Apis Mellifera Bee Hives Using Deep Learning

This study presents an innovative method to analyze Apis mellifera behavior within bee hives using deep learning. This approach uses high-resolution video captured from hive interiors to monitor the natural behaviors of worker and queen bees, analyzing their interactions, communicative dances, and productivity patterns. By leveraging deep learning with hyperparameter optimization on high-performance computing (HPC) systems, the method identifies both documented and undocumented behaviors. The resulting data allows for the evaluation of environmental impacts on colony dynamics and provides a framework for real-time behavioral tracking within large bee colonies. This approach not only enables efficient data processing and accurate detection of individual and group behaviors but also facilitates the creation of a comprehensive behavior model for Apis mellifera.