# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results
### Dataset Characteristics  
The reviewed publications consider various types of data for bee species discrimination that serve as input to the models, with the type of data influencing the model and its effectiveness for the bee species determination tasks. Generally, three data types have been employed: (1) images of the bees, either as static images or a time-series of images, i.e., videos, of the full body of the bees or specific parts of them; (2) acoustic features of the bees, including buzzing and flying sounds; and (3) movement of the bees, such as wing beat movement or traffic patterns of the bees. Figure 8 depicts a doughnut pie chart, with the inner layer representing the methods utilized and the outer layer representing the proportion of the different data types used. Images serve as the primary data type for bee species determination in 20 publications, or 76.9% of the overall publications utilizing them, including one publication using both image and movement; either utilizing full images of the bees (9 publications or 34.6%) [16; 32; 33; 34; 35; 36; 37; 38; 39] or focusing solely on wing imagery only (10 publications or 38.46%) [13; 20; 40; 41; 42; 43; 44; 45; 46; 47]. While publication [36] was unique in capturing video footage of bee species for their study, they opted for an unconventional approach by focusing the classification analysis on a single frame rather than exploiting temporal variations across multiple frames for enhanced accuracy. The utilization of wing images over other body parts is attributed to their unique venation patterns distinct to each species, their consistency compared to other bodily features, and the ease of imaging and analysis they offer. Collectively, these make the wing image an excellent and reliable morphological feature for species differentiation and suitable for automated analysis. Only one study relies on images of bees whilst in-flight for species determination [35], with the others rely on images when bees are relatively stationary. This highlights the challenges of capturing images of airborne bees due to their rapid movement, small size, and presence of potential occlusions. Only six publications, or 23% of the overall publications, utilized acoustic sound and movement of the bees for the species determination tasks. Acoustic sounds consider buzzing and flying sounds produced by bees. Though these sounds are interrelated, they exhibit unique features. Buzzing is primarily produced as a result of wing vibrations and may vary in frequency. It serves as a means of communication and temperature regulation within the hive. Flying sounds, on the other hand, are generated specifically during flight and are characterized by changes in pitch and intensity based on the bees' flight speed and wing size. Analyzing these auditory signals offers valuable insights into species differentiation. Some studies have also focused on the analysis of wing beats and bee traffic; categorized broadly as movement of the bees. Wing beats, the rapid flapping of bees' wings, have distinct patterns that can vary between species, and these variations can be crucial for the identification and differentiation between bee species. Bee traffic refers to the frequency and patterns of bees moving in and out of beehives, and may similarly provide insights for bee species determination purposes. Both wing beats and bee traffic patterns are valuable  
Figure 8: Number of publications utilizing different methods for bee species determination. data sources, offering unique perspectives in the study of bee species, their behaviors, and their ecological roles. Nearly all studies employing deep learning for bee species determination predominantly use image data, leveraging its capacity to handle complex, high-dimensional visual information, a strength of deep learning algorithms like CNNs.