# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results
### Dataset Characteristics
The geographical distribution of datasets illustrates that data collection is not diverse enough, with some regions unrepresented, despite their well-known activity in apiculture. This omission underscores the importance of regional diversity in datasets to  
Fifteen publications reported undertaking original data collection, a notable figure highlighting the significant efforts to amass new data in the field. This substantial number of data collection initiatives includes two studies that augmented their data with external sources. A novel method of data collection, crowdsourcing of data, was reported in one publication [32], showcasing an innovative strategy for data acquisition in a field that has limited amounts and diversity of datasets. Additionally, several studies leveraged datasets previously published, as well as data from public databases, including iNaturalist, BugGuide, BeeSpotter, the CREA Research Centre for Agriculture and Environment (CREA-AA), Kaggle, MS COCO, and the Morphometric Data Bank in Oberursel, Germany, reflecting a reliance on existing resources. Together, these points underscore the significant challenge of dataset scarcity, particularly in certain geographical regions, and the diverse methods researchers are employing to address this issue. Against the backdrop of 16 publications that embarked on original data collection, including one innovative crowdsourcing approach, a diverse range of methodologies from direct photography to acoustic recordings was utilized. This multifaceted approach not only highlights the creative strategies adopted to navigate dataset limitations but also emphasizes the commitment to expanding research into underrepresented geographical areas, significantly enriching the field of bee species determination with varied data collection methods. Various methods were employed for wing image collection. Bee wings were dissected [46], while others were merely immobilized using an icebox for a less invasive approach [20]. To enhance the detail of these images, researchers used optical magnification tools, including stereomicroscopes [45], microscopes [20], and magnifying glasses [46], although some images exhibited salt and paper noise [46], making the task more challenging. These instruments are essential in research for enhancing the visibility of small details and are particularly useful in the study of bee morphology, where a precise examination of features like wing venation is crucial for species determination. Some researchers have also taken direct camera shots without magnification of the wings, capable of achieving an impressive accuracy of 96% [44]. For full-body bee images, researchers employed various innovative techniques, including the use of trap cameras positioned at hive entrances [33; 42] and sticky yellow plates to capture bees [39]. Additionally, infrared (IR) imaging was utilized [35], taking advantage of its low light impact, which is crucial for capturing images without altering the natural behavior or appearance of bees, some of which may vary in color with age. These methods enable detailed observations with minimal disturbance, facilitating the collection of valuable data for classification and study. Acoustic data, including buzzing and flight sounds, were recorded using both general-purpose microphones [12] and specialized recorders such as the SongMeter SM2 [49]. One study employed wildlife acoustic recorders designed for capturing natural sounds [49], though no significant difference in performance was noted across these methods. Additionally, wing beat sounds were detected using optical sensors in two studies [48; 50], emphasizing the need to monitor environmental factors such as temperature and humidity, which influence wing beat frequency. This approach underlines the importance of considering various environmental conditions in accurately capturing bee movements and sounds.