# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 2 Materials and Methods
### Search Strategy  
A systematic search was conducted in the Web of Science ([[https://mjl.clarivate.com/](https://mjl.clarivate.com/)]([https://mjl.clarivate.com/](https://mjl.clarivate.com/)) (accessed on 13 November 2023)) and Elsevier Scopus ([[https://www.scopus.com/](https://www.scopus.com/)]([https://www.scopus.com/](https://www.scopus.com/)) (accessed on 13 November 2023)) databases, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement [27]. These databases are recognized as two of the most comprehensive academic databases, offering extensive publication search capabilities and providing utility in the ranking of journals based on productivity and citation impact; thus, they are capable of providing a robust foundation for a bibliometric analysis [28]. The search protocol targeted original research articles published in the English language that employed ML techniques for the detection or classification of honeybee and stingless bee species. Due to their significant ecological, economic, and scientific importance, these species were the specific focus. To maintain a clear focus, the review excludes non-research materials such as book chapters, conference abstracts, white papers, case reports, editorials, review papers, and technical reports. Keyword searches were conducted using the advanced search functionalities of two major databases, focusing specifically on the title, abstract, and keywords of publications. The keywords, detailed in Table 1, were strategically selected to balance precision and breadth, ensuring relevance without excluding pertinent studies. This targeted approach was critical to avoid the pitfalls of both overly narrow and overly broad keywords, which could lead to missing relevant papers or retrieving a large volume of irrelevant material, respectively. The selected keywords emphasize studies on the identification and classification of honeybees and stingless bees using machine learning techniques, reflecting the crucial roles these species play in agriculture and biodiversity, and their cultivation for valuable bee products. The search was particularly aimed at capturing publications that address the challenges of species determination and the application of machine learning methodologies in this context. Extracted data included the publication titles, abstracts, keywords, authors and their affiliations, publication years, and citation counts up to the date of the search. The searches on the two databases were conducted on 13 November 2023, and hence, publications indexed in the databases up to the date of the search were included. In total, 117 and 164 publications were captured from the Web of Science and Scopus databases, respectively. Fifty-five publications were identified as duplicates, appearing in search results from both the WoS and Scopus databases, and were subsequently removed. Another publication, which was not available on the internet, was also removed. A total of 225 publications were considered for the screening stage. A flow chart of the PRISMA declaration figure showing the flow of the search is presented in Figure 1.  
The publications, particularly, their titles and abstracts, were perused and thoroughly analyzed to ensure relevance to the topic, with 193 publications deemed irrelevant and another three publications excluded due to accessibility issues with the full texts. After screening the full texts, an additional three articles were identified as unrelated because