# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results
### Methods for Bee Species Determination
#### 3.3.1 Publications Utilizing Shallow Learning (SL) Only for Species Determination
A low-pass 12 kHz filter was applied to remove noise before features were extracted using Mel Frequency Cepstrum Coefficient (MFCC) from the sounds. The extracted features were then fed onto an SVM classifier for bee identification. The study highlights that the fundamental frequency of flight sounds varies by species, and the different acoustic properties, such as harmonic components, can significantly differ between species and background noise. Similarly, [PERSON] et al. [49] focused on using ML to recognize tomato-pollinating bees from their buzzing sounds, collecting flight and sonication sounds with a microphone. After preprocessing, MFCC features were extracted and various classifiers were tested, with SVM showing better performance than others, such as LR and RF. However, the overall performance was relatively low. The study emphasized strong relationship between sonication sounds and species, suggesting that further research may be beneficial, especially with larger sample size and different learning systems for improved species determination performance. Three publications specifically applied SL algorithms for bee species determination based on a movement analysis, with two of these publications utilizing wing beat patterns, as captured by more affordable optical sensors [48; 50]. [PERSON] et al. [48] focused on the hierarchical classification of flying insects, notably bees and wasps, under changing environmental conditions. The research, conducted in Brazilian fields, focused on wing-beat data to extract features related to the energy sum of frequency peaks, the harmonics positions, the spectrum complexity, and the fundamental frequency of wing beats, together with environmental variables such as temperature, humidity, and time of day, noting the significant impacts of temperature and humidity on wing beat sounds. A variety of hierarchical classification approaches, including flat, local (local classifier per node and local classifier per parent node approach), and global approaches, were explored, particularly emphasizing local classifiers for node-based and parent node-based approaches alongside hybrid models. These approaches leverage SL algorithms, KNN, MLP, NB, RF, and SVM, which were chosen for their abilities to manage the complexity and variability of environmental impacts. The local classifier for the node-based approach utilizing MLP gave the highest performance. Additionally wing-beat data, which were influenced by environmental factors such as temperature and humidity, were leveraged. [PERSON] et al. [50] successfully classified seven thermopteran species, including _Vespa velutina_ [PERSON], 1836, bees, and wasps, by employing Power Spectral Density (PSD) features with an RF algorithm. The RF model, utilizing a 14-value PSD peak and valley as features, demonstrated notable classification accuracy. Despite the data collection conducted inside an entomological tent, potentially affecting the flight characteristics of the bees, the recorded fundamental frequency of the wing beats in the study was in agreement with other reported studies. Additionally, the publication aligned with other findings that wingbeat frequencies inversely correlate with body size in Hymenoptera species. Together, these publications showcase sophisticated methods for leveraging SL in ecological studies, underscoring the significant potential for accurate species identification and ecological monitoring through wing-beat analysis amidst environmental fluctuations. The foraging pattern of bees has been used to discriminate between species of bees, specifically through the analysis of the bees' arrival and departure times as they forage [51]. Individual bees were uniquely tagged with Radio Frequency Identification (RFID) transponders, and an RFID reader recorded their movements to and from the hive.