# Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
###### Abstract  
In 2014, we designed and implemented BeePi, a multi-sensor electronic beehive monitoring system. Since then we have been using BeePi monitors deployed at different apiaries in northern Utah to design audio, image, and video processing algorithms to analyze forager traffic in the vicinity of Langstroth beehives. Since our first publication on BeePi in 2016, we have received multiple requests from researchers and practitioners for the datasets we have used in our research. The main objective of this article is to provide a comprehensive point of reference to the datasets that we have so far curated for our research. We hope that our datasets will provide stable performance benchmarks for continuous electronic beehive monitoring, help interested parties verify our findings and correct errors, and advance the state of the art in continuous electronic beehive monitoring and related areas of AI, machine learning, and data science. electronic beehive monitoring; audio classification; image classification; video processing; audio datasets; image datasets; video datasets, weather datasets; machine learning; data science; apis mellifera +
Footnote †: 2021: 11, 4632. [[https://doi.org/10.3390/app11104632](https://doi.org/10.3390/app11104632)]([https://doi.org/10.3390/app11104632](https://doi.org/10.3390/app11104632))  
[PERSON] Audio, Image, Video, and Weather Datasets for Continuous Beehive Monitoring  
[PERSON]## 1 Introduction  
In 2014, we designed and built BeePi [1, 2, 3], a multi-sensor electronic beehive monitoring (EBM) system. While BeePi can be used to monitor bee traffic in the vicinity of beehives of various common designs (e.g., Dadant, Top-Bar, Langstroth [4]), all our EBM research has been conducted on honeybee colonies in Langstroth hives [5]. In this article, we use the terms _hive_ and _beehive_ to refer to a Langstroth beehive and use the terms _bee_ and _honeybee_ to refer to the _Apsis Mellifera_ honeybee [6]. The original BeePi monitor consisted of a raspberry pi 2 computer, a pi T-Cobbler, a breadboard, a waterproof temperature sensor, a pi camera, a ChronoDot clock, and a Neewer 3.5 mm mini lapel microphone placed above the landing pad of a Langstroth hive. All BeePi hardware components, after being soldered and connected with jumper cables, fit in a single Langstroth super (i.e., a wooden box of specific dimensions). Small holes were drilled in the super's walls for hardware ventilation. Since 2014 we have been using the BeePi platform to design audio, image, and video processing algorithms to analyze forager traffic in the vicinity of Langstroth hives [7, 8, 9, 10, 11]. With the exception of the raspberry pi computer model, which has been upgraded in some deployed BeePi monitors to pi 3 or to pi 4, we continue to use the same hardware design. Reproducibility and replicability have been two fundamental objectives of the BeePi project from its inception: other researchers, practitioners, and citizen scientists must be able to reproduce our experiments and replicate our designs at minimum costs and time commitments, which is why all BeePi monitors are built with off-the-shelf hardware components and the BeePi software algorithms are developed on top of open source packages with no license fees. Interested readers are referred to [1,2] for BeePi hardware design diagrams, photos, technical specifications, and assembly videos. Another fundamental principle guiding our research is the sacredness of honeybee space: the deployment of sensors cannot interfere with the natural cycles of honeybees. Thisprinciple prevents us from deploying sensors inside the beehive or directly on honeybees (e.g., RFID labels on foragers [12; 13]). To preserve the objectivity of our data and observations, we do not intervene in the life cycle of the monitored colonies. For example, we do not apply any chemical treatments (e.g., Varroa mite or hive beetle treatments) to our colonies or re-queen failing or struggling colonies.