# Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
## 3 Image Datasets  
In 2018-2019, we started designing algorithms to analyze omnidirectional bee traffic in videos taken in the vicinity of landing pads of Langstroth hives. In [9], we defined omnidirectional bee traffic as \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Model** & **Feat. Sel.** & **Num. Feat.** & **Test. Acc. (\%)** & **Valid. Acc. (\%)** \\ \hline BUZZ1 & RF (100 trees) & RFE & 5 & 99.82 & 99.08 \\ \hline BUZZ2 & KNN & SFS & 7 & 96.81 & 95.43 \\ \hline BUZZ3 & KNN & SFS & 6 & 83.21 & 97.54 \\ \hline BUZZ4 & SVM & RFE & 11 & 71.75 & 95.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Datasets, models, feature selection methods (Feat. Sel. ), numbers of features (Num. Feat. ), and test and validation accuracies on BUZZ1, BUZZ2, BUZZ3, BUZZ4. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Dataset** & **Model** & **RFE/SFS Features** \\ \hline BUZZ1 & RF (100 trees) & RFE: [5; 10; 13; 14; 15] \\ \hline BUZZ2 & KNN & SFS: [5; 12; 13; 14; 15; 18; 20] \\ \hline BUZZ3 & KNN & SFS: [5; 10; 12; 13; 14; 16] \\ \hline BUZZ4 & SVM & RFE: [3; 4; 5; 9; 11; 12; 13; 14; 18; 19; 20] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SFS-based features for best models on BUZZ1, BUZZ2, BUZZ3, BUZZ4; feature numbers correspond to the indices of the feature array extracted with pyAudioAnalysis library [21]. \"... the number of bees moving in arbitrary directions in close proximity to the landing pad of a given hive over a given period of time.\"  
We designed a two-tier algorithm (2 TA) to count bee motions in bee traffic videos in the vicinity of a Langstroth hive. The 2 TA combines class-agnostic motion detection (tier 1) with class-specific image classification (tier 2) (See Figure 3). Tier 1 generates a set of regions where moving objects may be present. Tier 2 applies trained class-specific classifiers (e.g., ConvNets and RFs) to the image regions centered around the motion points generated by tier 1 to detect the presence or absence of specific objects (e.g., bees) in the regions. In tier 1, we used three motion detection algorithms in OpenCV 3.0.0 (i.e., KNN [24], MOG [25], and MOG2 [26]). In tier 2, we used trained ConvNets, SVMs, and RFs. Each motion region generated in tier 1 is classified by a trained classifier into two classes: BEE or NO_BEE. We curated two image datasets to train, test, and validate the tier 2 classifiers. The first dataset, which we called BEE1, consists of 54,382 32 \(\times\) 32 images [27]. We manually labeled each of the 54,382 images with two categories: BEE, if the image contained at least one complete bee, or NO_BEE, if it contained no complete bee or only a small part of a complete bee (See Figure 4). The BEE1 images were obtained from 40 videos randomly selected from the video dataset of \(\approx\)3000 videos captured by four BeePi monitors deployed on four Langstroth hives (See Figure 5). All four monitors were deployed on hives with Italian colonies. Each video had a resolution of 360 \(\times\) 240 with a frame rate of \(\approx\)25 frames per second. Two monitors were deployed in an apiary in North Logan, UT and the other two in an apiary in Logan, UT, from April 2017 to September 2017. The two apiaries were \(\approx\)17 km apart. We randomly selected 19,082 BEE and 19,057 NO-BEE images for training; 6362 BEE and 6362 NO_BEE for testing, and 1801 BEE and 1718 NO_BEE for validation. We used the training and testing images for model fitting and the validation images for model selection. We ensured that the data for the training and testing datasets and the data for the validation datasets came from different hives. The second dataset, which we called BEE2, contained 112,879 images obtained from the videos acquired by four BeePi monitors on four Langstroth beehives with Carniolan honeybee colonies in Logan, UT in May and June 2018. All colonies were hired in late April 2018. A total of 5509 1-super videos and 5460 2-super videos were obtained with the BeePi monitors.