# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
###### Abstract
This process necessitates specialized tools and a skilled individual to accurately measure various morphological characteristics. Crucially, keen observation is required to discern subtle features such as pigmentation, facial details, genitalia, and pilosity [14; 15]. Fine scaling is normally required to determine species with high precision due to the high similarities among species. Given the subtle differences among species, many cannot be accurately identified in natural settings or from photographs without a direct, sometimes destructive, examination. This often requires the species to be collected, cleaned, pinned, and even dissected to allow a detailed inspection of their morphological features under a high-powered microscope. However, this process is time-consuming and increasingly viewed with disfavor, especially for sensitive species [16; 17]. Another proven powerful approach is the use of a molecular tool kit for bee identification, which incorporates various markers of the mitochondrial (e.g., tRNAleu-cox 2 intergenic region) and nuclear DNA (e.g., microsatellites and single nucleotide polymorphisms [SNPs]). Despite their proven accuracy, these techniques are rarely used by those who could benefit most--beekeepers and breeders, particularly those working with diverse species or engaged in conservation efforts. Precise species identification is essential for effective management and conservation, helping these professionals make well-informed decisions regarding species selection and management practices. The main obstacles to widespread adoption are the high costs, the necessity for expert knowledge, and the need for specialized equipment and reagents [18]. These financial and technical challenges severely restrict the routine use of molecular identification in beekeeping, underscoring a significant gap between the availability of advanced scientific tools and their practical applicability in the field. The complexity of bee species identification is exacerbated by the sheer diversity of species, estimated at around 20,000 globally [19]. This diversity presents a significant challenge for the limited number of experts in the field, with only about 50 taxonomists worldwide skilled in accurately identifying these species. This scarcity of experts under scores the need for more accessible and efficient identification methods to support the conservation and study of bee populations [20]. Advancements in computer-based technologies and artificial intelligence (AI) are stepping up to meet this need [21]. These technologies offer the promise of high-accuracy species identification with reduced human intervention, significantly enhancing the efficiency of bee monitoring efforts. The evolution of AI, in particular, has proven advantageous in fields where expert knowledge is scarce, providing valuable decision-making support. Consequently, the burgeoning field of machine learning (ML) applications in bee species identification has attracted considerable research interest. This shift towards leveraging ML for bee identification not only reflects the adaptability and potential of technology in biodiversity conservation but also underscores the importance of reviewing and understanding the diverse methodologies and datasets employed across the spectrum of existing studies. This paper presents a comprehensive literature review focused on the use of machine learning (ML) techniques for identifying bee species, with a particular emphasis on honeybees and stingless bees. It begins with a bibliometric analysis to explore authorship and citation patterns, which helps in understanding the research dynamics within this scientific area [22]. The review methodically examines relevant publications to provide an in-depth analysis of the data, methodologies, technologies, and performance metrics employed.