# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 4 Discussion
Movement patterns of bees serve as a distinguishing feature for their identification and species determination, leading to the utilization of this criterion in three publications. These studies exclusively focus on analyzing the movement of bee wings or the bees themselves. Employing shallow learning (SL) algorithms such as MLP and Random Forest (RF), these models achieve higher performance compared to sound-based models. However, despite their effectiveness, they are limited in their scope, classifying fewer bee species. This highlights the potential for further research to enhance accuracy and expand the coverage of movement-based bee identification models. While computer vision techniques have been extensively employed in stationary bee image classifications, there is a notable scarcity of studies proposing computer vision-based approaches to flying bees. Among these, a single study stands out for its innovative methodology, utilizing 3D trajectories classified by SL algorithms and IR images classified by DL models, with the integration of SL and DL techniques allowing for a comprehensive analysis of bee behavior and morphology. The final results were obtained through the aggregation of outcomes from both models. The high performance of the image-based bee identification model underscores its potential for development into mobile or web-based applications that allow stakeholders--including novice researchers, breeders, and conservationists--to identify bee species from uploaded images. This technology not only democratizes access to expert-level species identification but also enhances the accuracy and efficiency of monitoring bee populations and their health. The advancement of the multi-evidence-based model, which now includes capabilities for identifying bees in flight, offers significant benefits for beekeepers. It enables real-time monitoring of bee activities and the identification of potential threats such as invasive species or pathogens. This is particularly advantageous for proactive bee conservation efforts and detailed ecological research, as it allows for immediate intervention and informed decision-making. Furthermore, enhancements in the acoustic-based system contribute substantially to our understanding of bee pollination patterns. By accurately capturing and analyzing the sounds associated with bees pollinating specific flowers, this system can pro vide invaluable data for botanists and agricultural professionals. The insights gained can lead to improved crop management strategies and contribute to broader ecological health by ensuring effective pollination, which is crucial for the survival of many plant species. The ongoing development of species identification methods using diverse approaches--visual, acoustic, and behavioral--promises to expand the toolkit available for various stakeholders. These tools are not only pivotal for research and conservation but also have practical applications in agriculture and bio-surveillance. The integration of these technologies into user-friendly platforms can significantly enhance engagement and participation in bee conservation initiatives, contributing to sustainable practices and biodiversity preservation. In light of these developments, future research should focus on refining these models, expanding their applicability under diverse environmental conditions, and enhancing their integration with existing digital agriculture and conservation tools. This will further solidify the role of advanced machine learning techniques in environmental science and ecological monitoring. Recent advancements in bee identification methods have significantly reduced the need for human intervention, marking a pivotal shift toward automation. The evolution of computational technologies has empowered the integration of DL techniques for bee species determination, while the adoption of innovative approaches such as the Visual Transformer method signifies ongoing updates in ML technologies for bee identification.