# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results
### Dataset Characteristics
Conversely, shallow learning methods, utilized in only half of their respective studies for image-based classification, also tap into movement and acoustic data, reflecting their adaptability to simpler, less complex data types. The preference for deep learning in image-based species determination is due to its exceptional capability to detect and learn complex patterns and features from visual data, a critical factor for achieving precise classification. On the other hand, species determination tasks based on bee movement exclusively utilized shallow learning algorithms. This singular reliance on shallow learning for processing movement data underscores its efficiency and adaptability in interpreting bee motion behaviors. The volume and diversity of datasets play crucial roles in ML. Large datasets improve the robustness of ML models, allowing them to learn and generalize better, whilst diversity in data, especially in class representation, is vital for ensuring that the model can accurately identify and classify a wide range of bee species. Ideally, the dataset should also be well-balanced, with each class equally represented. This prevents biases during training of the model and consequently enhances the overall performances of the species determination task. Figure 9 illustrates the number of species considered in each publication, with size of the dataset represented as dimensions of the circle. Different data types are visually distinguished by color coding as follows: purple represents acoustic data, blue and brown denote image and movement data, respectively, and green indicates image and movement data combined. The size of each circle is proportional to the dataset size, with larger circles corresponding to larger datasets. The image data type constituted the most extensive and diverse collection of datasets. [PERSON] et al. [40] focused on classifying the highest number of species, with 127 species of bees represented using wing images, while [PERSON] and [PERSON] [12] relied on 89,000 full images of bees, representing the most comprehensive and diverse datasets, respectively. These are indeed expected, as it is much easier to obtain good dataset of images as compared to other data types, especially with the fast progression of mobile technology. A study conducted by [PERSON] et al. [48] utilized more than 56,000 samples of the movement data type to classify a total of seven species. It is interesting to note that the diversity of bees considered does not necessarily correlate with the volume of dataset, with some researchers relying on smaller dataset to classify larger number of species, and vice versa. Figure 10 shows the distributions of publications focusing on the different species of honeybees, stingless bees and others, including bumblebees, hornets, and other insects. Seventeen publications focused on honeybees, either to identify different species and subspecies of honeybees, or in conjunction with other insects. On the other hand, only five publications considered a dataset of different species of stingless bees. This is despite the diversity of stingless bee species in the world amounting to more than 600 species and their economic potential in producing high-valued bee products. The geographical origins of the dataset hold significance, with some bee species endemic to specific regions only. Figure 11 illustrates the origin of the dataset considered by only 13 of the studies; 13 studies did not disclose the geographical source of their dataset. Furthermore, some studies employed datasets originating from multiple geographical sources. Four studies considered datasets originating from Brazil, with one study in conjunction with species from Germany, the United States and China. Datasets originating from the United States, China, and Spain were featured in two publications each, while datasets from twenty-one other countries were each considered in only one publication.