# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results  
### Bibliographic Analysis  
A total of 281 publications were initially extracted from both databases: 117 and 164 publications from the Web of Science and Scopus databases, respectively. This was reduced to 26 relevant publications after accounting for duplications, irrelevant publications, and other exclusions. Only publications indexed in the databases on 13 November 2023 were included in this study. The categorization of methodologies across the 26 studies reveals a prevailing preference for deep learning techniques. Deep learning approaches, including CNNs and RNNs, are utilized in 12 publications, constituting 46.15% of the total publications. Five publications or 19.2% of the total utilized both deep and shallow learning for their classification. On the other hand, only nine studies rely solely on shallow learning, with the earliest shallow learning application tracing back to 2001. This trend underscores a significant shift towards the adoption of complex ML techniques in the research field. A detailed summary of each publication, categorized by the methods employed, is listed in the Supplementary Materials, specifically Tables S1-S3. Figure 2 illustrates a temporal analysis of the 26 publications on machine learning for bee species determination, showing a significant trend in research focus and methodologies from 2014. An increasing number of annual publications is observed, particularly from 2020. This pattern suggests growing interest within the research community. It is noted that since the search was performed at the end of the year 2023, the publication data for 2023 may be incomplete as it normally takes a few months for publications to be indexed in the databases. This rising trend is further reinforced by the annual citation data shown in Figure 3, indicating a substantial growth in interest post-2017 and emphasizing the field's dynamic and emerging nature. Additionally, the trend towards deep learning (DL) has become particularly pronounced post-2020, as evidenced by the year-on-year increase in publications utilizing DL. An analysis of the most frequently used keywords by authors offers valuable insight into the prevailing research interests and priorities within the scientific community [31]. Consequently, scrutinizing the author-defined and index keywords in the selected publications is crucial for understanding the current trends and focal points in the field. A total of 272 keywords were identified and setting a minimum co-occurrence of three for these keywords to be included in the analysis resulted in 21 most used keywords, as shown in Table 2. The accompanying keyword co-occurrence network in Figure 4 illustrates each keyword as a node, with the node size scaled to reflect the frequency of keyword occurrences. Links between nodes represent the co-presence of the keywords in publications. Moreover, the color coding in Table 2 and Figure 4 distinguishes different clusters, with each color representing a group of keywords that frequently appear together. This method highlights thematic connections between keywords, facilitating an intuitive understanding of the main research areas and their interrelationships within the field. Figure 4: Co-occurrence network of keywords. Figure 3: Annual citations of the publications. Figure 2: Yearly publications on bee species determination tasks using machine learning. Five clusters are formed by the keywords, with significant correlations observed between keywords belonging to the same cluster. Signifying the intersection of biological and computational sciences, the red cluster highlights the fundamental aspects of bee study (taxonomy and pollination) alongside advanced analysis techniques (machine learning and computer vision). Prominence is given to \"bee\" and \"machine learning\", with occurrences of nine and eight, respectively, within this cluster, underlining their critical roles in the review.