# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
###### Abstract  
Given the vast diversity of bee species and the limited availability of taxonomy experts, bee species identification has become increasingly important, especially with the rise of apiculture practice. This review systematically explores the application of machine learning (ML) techniques in bee species determination, shedding light on the transformative potential of ML in entomology. Conducting a keyword-based search in the Scopus and Web of Science databases with manual screening resulted in 26 relevant publications. Focusing on shallow and deep learning studies, our analysis reveals a significant inclination towards deep learning, particularly post-2020, underscoring its ability to handle complex, high-dimensional data for accurate species identification. Most studies have utilized images of stationary bees for the determination task, despite the high computational demands from image processing, with fewer studies utilizing the sound and movement of the bees. This emerging field faces challenges in terms of dataset scarcity with limited geographical coverage. Additionally, research predominantly focuses on honeybees, with stingless bees receiving less attention, despite their economic potential. This review encapsulates the state of ML applications in bee species determination. It also emphasizes the growing research interest and technological advancements, aiming to inspire future explorations that bridge the gap between computational science and biodiversity conservation. machine learning; bee species; systematic review (PRISMA); bee identification; species classification; species determination +
Footnote †: journal:  
0000-0001-5000-6999-2227][PERSON], A.G.A., Abas, P.E. (2020) 22 May  
in weather, genetically altered crops, and competition for limited resources [8]. The decline in bee populations necessitates vigilant monitoring and management to ensure their survival and continued contribution to ecosystems and agriculture [9; 10]. This process involves not just understanding the spatio-temporal patterns, biodiversity, and habitats of bee species but also their health and behavior in relation to environmental factors. For instance, beekeepers commonly monitor the foraging behavior of bees to assess the availability of food, colony age, and pesticide impact [11]. On the other hand, in greenhouse environments where bees are specifically bred for pollination, farmers closely monitor bee activity to determine the optimal timing for replacing beehives. This practice ensures that pollination occurs efficiently, contributing to the health and productivity of the crops being cultivated [12]. Identifying bee species and subspecies is an essential activity that is intimately linked to monitoring efforts. This identification is pivotal for effective breeding, comprehensive conservation strategies, and optimized agricultural practices, which collectively influence the sustainability of bee populations and the overall efficiency of pollination services. Accurately determining the most suitable and economically viable species for various environmental conditions and specific purposes, such as greenhouse cultivation, is critical. In the realm of apiculture, the significant variation in honey production across different bee species and subspecies further highlights the necessity of precise bee identification. This precision is vital not only for supporting effective breeding programs and ensuring product certification but also for enhancing conservation efforts. Ultimately, the precise identification of bee species underpins efforts to optimize agricultural and apicultural productivity, underscoring its importance in maintaining ecological balance and promoting sustainable practices [13]. The conventional techniques of species identification rely on the morphological characteristics of tiny body parts of bees, including the size of various body parts, wing venation, pilosity, and pigmentation.