# 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
This innovative approach extracts features from RGB images using CNN layers, projects them into linear embeddings to convert them into a sequence, and then embeds the position and feeds it to the transformer encoder. Finally, the output of the transformer encoder is fed into the fully connected layer to yield the results. Trained to identify bees and detect varroa mites and pollen on them, it is an efficient strategy, as the same model makes two different decisions, achieving 92.45% accuracy in bee species determination and up to 99.18% accuracy in detecting fine-grained objects like varroa pests and pollen on bees. BeeNet surpasses other models, including ResNest, EfficientNet and Vision Transformer, in both bee surveillance and health monitoring tasks. Within the eight publications using images for bee species determination, four publications specifically employed object detection methods. These methods prioritize identifying the precise location of bees within images by delineating their bounding boxes, subsequently classifying their species. This approach, which integrates object detection as a preliminary step before species determination, enhances the accuracy and efficiency of species identification by focusing on relevant features within designated areas. [PERSON] et al. [36] developed a visual-based expert system to aid beekeeping tasks, focusing on the image segmentation of Meliponine bees, a tribe of stingless bees popular in Malaysian beekeeping. The challenge is the accurate detection and identification of these small bees in natural surroundings. The paper employs the Faster R-CNN, a deep learning framework composed of the Region Proposal Network (RPN) and Fast Region-based CNN (Fast R-CNN), to segment Meliponine bees from background imagery. With a dataset of 400 image frames from a local Meliponine farm, the model achieves promising results with a segmentation accuracy of around 74%. This work is a step towards creating a visual-based expert system for bee species identification. Another study also leveraged on Faster R-CNN, but with ResNet101+FPN for bee species determination, particularly focusing on small object detection [32]. It tackles the challenge of identifying bees within crowdsourced images from the BeeSpotter database, characterized by varied image qualities and the presence of multiple bees, specifically targeting _A. mellifera_, _Bonbus griseocollis_ [PERSON], 1773, and _Bonbus impatiens_ [PERSON], 1863. The Feature Pyramid Network (FPN) was utilized within the Faster R-CNN framework. It serves to enhance the model's ability to detect Meliponine bees at various scales by creating a multi-scale feature pyramid from a single input image. The approach enhances identification accuracy by isolating bees using object detection, significantly reducing the need for manual image annotation. Initially trained with hand-annotated images and later augmented with machine-labeled data, this methodology successfully classifies two genera and related bee species, achieving a classification accuracy of 91%. [PERSON] et al. [42] aimed to address the challenge of accurately detecting and classifying common species, like Chinese bees, wasps, and cockroaches, at beehive gates in natural environments. To solve multi-target and multi-scale problems, the authors proposed an improved RetinaNet target detection network, DY-RetinaNet. This model integrates a symmetric structure Bidirectional Feature Pyramid Network (BiFPN) layer in the place of Feature Pyramid Network (FPN) for multi-scale feature fusion and employs Complete Intersection Over Union (CIOU) loss for precise small target localization. Additionally, a dynamic head framework was added to enhance multi-scale recognition in multi-target scenarios.