## 6 Conclusions  
In this investigation, we proposed, implemented, and partially evaluated a two-tier method for counting bee motions in video analysis of omnidirectional bee traffic. Our method couples motion detection with image classification so that motion detection acts as a class-agnostic object location method that generates a set of regions with possible objects and each such region is classified by a class-specific classifier or an ensemble of classifiers such as a random forest. The method has been, and is being field tested in multiple BeePi monitors, our multi-sensor EBM systems. Our evaluation of several DL and ML models on three large image datasets obtained from the data captured by different BeePi monitors indicates that image classification can be added to a repertoire of EBM algorithms. In our future work, we plan to work on correlating omnidirectional bee motion counts with ambient temperature readings and formalized human beekeeper observations. To ensure the replicability of the reported findings and to provide a performance benchmark for interested research communities and citizen science initiatives, we have made public our curated image datasets, BEE1 (54,382 images) and BEE2 (112,879 images), used in this investigation. The Supplementary Materials include four bee traffic videos with corresponding human motion counts, sixteen performance videos that illustrate how different system configurations process bee traffic videos, and four bee traffic videos with no human motion counts which we used to estimate if the proposed system can distinguish among different bee traffic levels. The following materials are available at [[http://www.mdpi.com/2076-3417/9/18/3743/s1](http://www.mdpi.com/2076-3417/9/18/3743/s1)]([http://www.mdpi.com/2076-3417/9/18/3743/s1](http://www.mdpi.com/2076-3417/9/18/3743/s1)). Conceptualization, Supervision, Project Administration, Resources: V.K. ; Software: V.K. and S.M. ; Data Curation: S.M. and V.K. ; Writing--Original Draft Preparation: V.K. and S.M. ; Writing--Review and Editing: V.K. and S.M. ; Investigation, Analysis: V.K. and S.M. ; and Validation: S.M. and V.K. This research received no external funding. The hardware components, bee packages, and beekeeping equipment used in this study have been funded, in part, by the Kicksarter fundraiser [42]. We are grateful to [PERSON] for helping us with data curation, validation, and software testing. We would like to thank [PERSON] and [PERSON] for helping us with labeling and curating BEE1 and [PERSON] for helping us with beehive inspections and beekeeper log entries. [PERSON], [PERSON], and [PERSON] allowed us to use their private property in northern Utah for our longitudinal EBM experiments and field tests. We are grateful to all students who participated in the deep learning competition organized by the first author in October-December 2018. Finally, we would like to express our deep gratitude to the donors who generously contributed to our Kicksarter fundraiser [42]. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} EBM & Electronic Beehive Monitoring \\ DL & Deep Learning \\ ML & Machine Learning \\ ConvNet & Convolutional Neural Network \\ ReLU & Rectified Linear Unit \\ FC & Fully Connected \\ R-CNN & Regions with ConvNets \\ GS & Grid Search \\ CV & Computer Vision \\ SVM & Support Vector Machine \\ KNN & K-Nearest Neighbor \\ RF & Random Forest \\ NT & No Traffic \\ LT & Low Traffic \\ MT & Medium Traffic \\ HT & High Traffic \\ \end{tabular}   
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Pollen bearing honey bee detection in hive entrance video recorded by remote embedded system for pollination monitoring. _ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci._**2016**, \(3\), 51-57. [CrossRef]
* [PERSON] et al[PERSON] (2012) [PERSON]; [PERSON] [PERSON]; [PERSON] ImageNet classification with deep convolutional neural networks.