# 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
These approaches underscore the potential of fine-tuned DL models, as well as class weighting, in ecological species identification tasks. In the realm of bee species determination using wing images, deep learning (DL) models have shown superior performance over shallow learning approaches in handling wing-based data, largely attributed to the capacity of DL to manage complex patterns. Among various convolutional neural networks (CNNs) tested, Inception-ResNetV2 consistently demonstrated robust performance across the three studies. Nonetheless, a range of other CNN architectures also yielded commendable results, underscoring the effectiveness of DL techniques in accurately identifying bee species from wing imagery. Eight publications employed full-body bee images alongside deep learning (DL) techniques for species determination, expanding beyond wing-only imagery. [PERSON] et al. [34] employed a 2-layer convolutional neural network (CNN) to classify honeybee species using over 5000 images, aiming to protect and preserve various species by enabling early disease detection. To combat dataset imbalance and enhance the model's training, it incorporates data augmentation techniques such as image zooming, flipping, rotating, and shrinking, alongside the Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples based on KNN. The approach achieved an 86% accuracy rate in species determination, underscoring the potential of automated species determination in conserving bee populations and health. [PERSON] et al. [38] presented a concatenated approach for classifying bees, insects, wasps, and others using images. It employs Principal Component Analysis (PCA) for the dimensionality reduction of features fed into four pre-trained convolutional neural networks (CNNs): VGG, ResNet, XceptionNet, and EfficientNet, thereby leveraging on transfer learning and the selective reduction of dimension. The outputs were then combined and fed into fully connected layers for the final classification. This technique, emphasizing the effectiveness of PCA efficiency in reducing feature dimensions, demonstrated superior results with an overall accuracy of 96.39% compared to using the individual CNNs alone, except for EfficientNet. Additionally, the concatenated approach surpassed even the EfficientNet model in classifying the others category, showcasing the potential of combining PCA with deep learning for image-based species identification. Four convolutional neural network models, ResNet, Wide ResNet, InceptionV3, and MnasNet, were examined for species determination of 36 North America bumblebee species using over 89,000 images [16]. InceptionV3 was selected for its optimal balance of accuracy (91.6%) and speed, despite Wide ResNet's slightly higher accuracy, for the subsequent development of the BeeMachine web app, a simple web application that allows users to identify species using their own images. Incorporating the models' performance nuances, the study found that classification accuracy was influenced by each species' variability of appearance, the volume of training images, and image quality. Additionally, incorporating geographic data has been suggested to enhance model accuracy, acknowledging the variation in the appearance of bumblebees across different regions. This insight emphasizes the complex factors affecting automated identification and suggests integrating spatial data as a potential strategy to improve the precision of models like InceptionV3 and Wide ResNet in ecological applications like BeeMachine. BeeNet, a deep learning model designed for bee surveillance, aims to enhance the feature representation of economically significant bee species and identify objects crucial for bee health monitoring, such as parasites or pollen [37]. The model utilizes a variant of the transformer encoder-decoder architecture, incorporating image features extracted from ResNet50.