# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 4 Discussion  
Out of 281 publications reviewed from WoS and Scopus, 26 publications were selected, with the first one dating to 2001, by employing shallow learning techniques. Over time, both shallow and deep learning algorithms have been increasingly utilized for bee identification and species determination tasks, with a notable rise in the use of DL. This trend is reflected in the growing number of publications and citations annually in this field. Interestingly, keywords related to SL, DL, and computer vision, which were not part of the search terms, have emerged, highlighting the significant role of computer vision in bee identification research. Brazil stands out for its high number of collaborative studies, while Portugal is recognized for collaborating with the largest number of countries. Notably, CEDRI has conducted significant collaborative research in bee identification. These findings underscore the increasing scholarly interest in this area, as evidenced by the rising number of publications and citations. Recent advancements in data collection methods have minimized their impacts on bees, leading to an increase in image-based datasets compared to other types. Many of these image datasets have now been made publicly available, likely contributing to the surge in image-classification research related to bees. Most research efforts have focused on honeybees, with relatively few studies addressing stingless bees, despite the increase popularity of bee products related to stingless bees. While the diversity of bee species is globally recognized, the data supporting this diversity are often limited to a few countries. To address this data scarcity, innovative crowdsourcing methods have been introduced, enabling the gathering of data worldwide. This approach not only expands the dataset but also increases its diversity, which are essential for developing robust machine learning models. Moreover, with access to publicly available datasets, researchers can focus more on refining machine learning models rather than on the arduous task of data collection. Admittedly, however, datasets are still limited, both in terms of volume and the diversity of species. The image-based data type has emerged as the predominant method in bee research studies, with a lesser focus on acoustic and movement data. SL techniques, particularly using images, have been widely utilized for analyzing bee wings. This method capitalizes on the straightforwardness of extracting features and distinguishing the venation patterns on wings. Most of these studies leverage landmarks that are easily identifiable through image processing techniques and accessible even to non-experts. Various research efforts have employed landmarks, alongside color and SIFT features, utilizing SL models like SVM, MLP, and KNN for species determination tasks. The development of CNN models has advanced bee identification further, shifting from wing-only images to full-body imaging. This progression not only lessens the impact on bees during data collection but also enables these deep learning-based models to classify an impressive range of up to 129 species with remarkable accuracy. Combinations of SL and DL models have also been employed in both sequential and parallel manners. In the sequential approach, a specific part of the identification process is managed by one model, with its findings utilized by another, whereas, in the parallel approach, models are fed the same or different datasets, with the outcomes eventually combined. This strategic application of SL and DL models enhances the precision and efficiency of bee species identification processes. Three studies focus on utilizing flying and sonication sounds for bee species identification. Among them, two employ shallow learning techniques, utilizing MFCC features and SVM classification. However, the third study utilizes Log Mel-spectrogram features with a pre-trained EfficientNet V2 model. Despite this advanced approach, its performance is notably inferior to the other two studies.