# 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  
Deep learning (DL) techniques dominate bee classification research, with a total of twelve publications exclusively utilizing DL techniques. Eleven of the twelve publications leveraged image-based data for species determination, whilst only a single study explored the use of acoustic sound for this task, indicating a strong preference for visual over auditory data in current DL applications within the bee species identification field. [PERSON] et al. [3] aimed to enhance automatic recognition of pollinating bee species using DL models, focusing on their buzzing sounds. They recorded sonication and flight sounds as bees approached flowers, and then transformed these sounds into Log Mel-spectrograms for analysis by CNNs like EfficientNet V2 and Pre-trained Audio Neural Networks (PANN). Data augmentation techniques such as mixup, SpecAugment, and Randomly Truncated were used to address data imbalance and cross-validation was employed to prevent overfitting. These methods significantly improved the classification performance, with EfficientNet V2 achieving the highest accuracy of 58.04%, highlighting the potential of DL in bioacoustic species identification and the importance of data augmentation and pre-training. Three publications utilized wing images with DL for the bee species determination task [13; 40; 43]. [PERSON] et al. [13] considered four different DL methods, InceptionResNet V2, InceptionNet V3, MobileNet V2 and ResNet 50, for the determination of seven subspecies and one hybrid species of honeybees based on their wings. All models achieved over 92% accuracy, with the InceptionNet V3 giving the highest accuracy of 99.12%. Different DL models, including LeNet-5, AlexNet, ResNet50, Inception v3, InceptionResNetV2, VGG-16, and VGG-19, were evaluated for the determination of 19 different species of bees and 10 different species of butterflies based on images of their wings [43], with a particular focus on bees due to the relevance to this review. The bee wing dataset, being notably small and unbalanced, posed a challenge as DL models were especially designed for large datasets. Subsequently, data augmentation techniques, including perspective skewing, elastic distortion, rotation and shearing, and transfer learning, were used to address this challenge. Among the models, the pre-trained InceptionResNetV2 model with data augmentation stood out, achieving an accuracy 94.40% for bee determination, highlighting the benefits of data augmentation and transfer learning in improving performance on relatively small ecologically derived datasets. [PERSON] et al. [40] focused on monitoring insect populations for ecological health, putting forth the identification of species as one of the problems in this domain. DeepABIS, an advancement of the Automated Bee Identification System (ABIS), was introduced for mobile field investigations and species identification of live bees. The system uses advanced CNNs, particularly, the B-CNN, MobileNet V2 and Inception-ResNet. To artificially increase the training dataset, data augmentation techniques, including rotate, zoom, shear, flip vertically (flipUD), and pepper, were adopted, in conjunction with transfer learning. The loss function was adapted by integrating class-level weights, calculated from the sample prevalence, to address class imbalance in the wing bee datasets. Notably, while the Inception-ResNet initially illustrated better performance with an accuracy of 88%, confirming the results of the previous study [43], MobileNet V2 surpassed the performance of the other models with an accuracy of 93.95% when enhanced with transfer learning, albeit only slightly better than the Inception-ResNet-reported accuracy of 93.16% with transfer learning. Additionally, the use of class weighting was also shown to increase accuracy, with a 1.06% increase in the case of MobileNet v2.