# 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
Experimental results demonstrated the superior network performance of DY-RetinaNet, with a mean average precision (mAP) value of 97.38% when using ResNet-101-BiFPN as the backbone network as compared to other algorithms, including SSD, YOLOV3, Faster R-CNN, Mask R-CNN, FCOS and ExtremeNet, indicating a significant accuracy improvement over the initial RetinaNet model. Some of the authors in the publication [42] utilized the Mini-EfficientDet neural network, optimized through transfer learning, to identify common species at Chinese beehive gates in Fujian Province, including bees, wasps, and cockroaches [33]. The Mini-EfficientDet model compresses the original EfficientDet by reducing the multiple stacking of BiFPN layers in the original EfficientDet model to one layer and introduces a category imbalance feature to enhance small target recognition without sacrificing accuracy. These approaches also reduce memory usage. Additionally, the dataset was enhanced by adjusting brightness, applying motion blur based on point scatter principles, and incorporating white Gaussian noise. Evaluations conducted on the MSCOCO2017 dataset and a custom dataset tailored for beehive species demonstrated the effectiveness of the model, with detection accuracies reaching 98.66% for Chinese bees, 83.71% for cockroaches, and 82.06% for wasps. These results establish a solid foundation for the future development of species invasion alert systems. 3.3. Publications Utilizing Both Shallow Learning (SL) and Deep Learning (DL) for Species Determination  
In the development of a novel model, a combination of both shallow learning (SL) and deep learning (DL) algorithms has also been explored to harness their respective strengths. This model stands out by combining the intricacies of DL with the straightforwardness of SL, aiming to achieve superior predictive performance. Some implementations fed the output from one algorithm type into the other, creating a synergistic effect, while others ran both algorithms in parallel on varied data types, later aggregating their results for the final decision. This innovative approach demonstrates a strategic integration of SL and DL to optimize accuracy and efficiency in ML-based bee identification. [PERSON] et al. [39] aimed to detect and quantify flying insects on yellow sticky traps using a Sony IMX219 8-megapixel sensor. Images were downsized from 3280 \(\times\) 2464 to 448 \(\times\) 448 pixels to reduce the computational load. The YOLO model, pre-trained on ImageNet and further trained on a dataset of 12,000 labeled images, was used to identify insects. An SVM model, trained with 7000 augmented images, classified insects into seven categories. Local features were extracted using the Histogram of Oriented Gradients (HOG), while global features (shape, texture, and color) were also analyzed. The model's reliability was ensured with 5-fold cross-validation, and the system was deployed on a Raspberry Pi. Results showed that global features alone provided higher accuracy than combined features, and the YOLO-SVM combination achieved 90.18% accuracy, outperforming other methods. This approach effectively monitors pest density in agriculture. [PERSON] et al. [35] proposed a novel approach using 3D trajectories to classify bees and non-bees. The study aimed to accurately identify two Vespa species and honeybees by analyzing 456,287 infrared images and 14,565 3D trajectories of _A. mellifera, V. velutina_, and _Vespa orientalis_ [PERSON], _1793_. Three models were trained: a shallow learning model using 3D trajectories from depth maps and RGB images, and two deep learning CNN architectures (Xception and Googlenet) trained on low-resolution IR images. Key features such as time of flight, roaming factor, velocity, and acceleration were extracted and processed using the ensemble bagged trees algorithm. The final decision was based on aggregating predictions from all three models, with class-specific weights assigned based on F-scores.