# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results
### Methods for Bee Species Determination
#### 3.3.2 Studies Utilizing Deep Learning (DL) Only for Species Determination
The highest accuracy of 97.1% was achieved by selecting 11 IR images per trajectory for model training, providing a robust method for identifying Vespa species near beehives. [PERSON] et al. [45] aimed to classify honeybee subspecies using geometric morphometrics of their right forewings. They used images of right forewings annotated with 19 key landmarks captured via a stereomicroscope and digital camera. Techniques included generating masks from landmarks to isolate the wing, enhancing features with Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gaussian filters, and using the SSD MobileNet v1 FPN coco model for wing segmentation. A U-Net model was trained on grayscale images for accurate landmark detection, followed by PCA and Procrustes normalization to reduce geometric variations. An SVM model classified the subspecies, achieving an 86.6% accuracy. The SSD MobileNet v1 FPN coco model had a mean average precision of 0.975, and U-Net showed high landmark detection precision at 0.943. A Flask-based web service was developed to provide access to the model, offering an effective framework for honeybee subspecies determination based on wing morphology. [PERSON] et al. [41] evaluated the DeepWings model (the result of earlier research by [PERSON] et al. [45]) using a dataset of 14,816 right forewing images from M-lineage (_Apis mellifera bienensis_ [PERSON], 1999 and _Apis mellifera Linnaeus, 1758) and C-lineage (_Apis mellifera carnica_ [PERSON], 1879) subspecies, collected from 2601 colonies across 15 countries. The study aimed to measure the model's performance in identifying colonies, compare these identifications with the distribution of endemic subspecies, and assess the match with molecular markers. Images from 26 subspecies were collected, but the focus was on five: Iberian, Dark, Carniolan, Italian, and Caucasian honeybees. DeepWings classified the images, rejecting some due to low resolution or noise. The model's classifications closely matched the endemic M and C lineages, with 71.4% and 97.6% accuracy, respectively. At the subspecies level, accuracy was 89.7% for Iberian, 41.1% for Dark, and 88.3% for Carnional honeybees. However, _A. carnica_ and _Apis caucasica_ [PERSON], 1916 could not be identified at the colony level. The agreement between DeepWings and molecular analyses was weaker in cases of genetic pollution. [PERSON] et al. (2020) developed an automated species identification system using data augmentation techniques to tackle limited training data. Their dataset included images of butterflies (Parides), bee wing venation (Osmia), and scarab beetle genitalia. The model training involved rotating images, applying Style-Generative Adversarial Network (StyleGAN) for data augmentation, using a pre-trained VGG-16 network with Max pooling for feature generation, applying PCA to reduce overfitting, and using SMOTE for class imbalance. These processed features were then fed into an SVM model. This approach improved accuracy compared to using the original VGG-16 model alone, achieving 80.07% for Pleophylla, 85.16% for Schizonycha, 85.90% for Osmia, and 98.06% for Parides, demonstrating its effectiveness in enhancing species identification with limited data.