# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 3 Results
### Methods for Bee Species Determination
#### 3.3.1 Publications Utilizing Shallow Learning (SL) Only for Species Determination  
Of the nine publications utilizing shallow learning methods, four publications utilized images, specifically, wing images, with two and three publications utilizing acoustic and movement data types for the species determination task. The review of four publications employing wing images for bee species determination showcases a variety of approaches, focusing on different features such as basal cells, landmarks, and vein skeletons, alongside various machine learning algorithms, including SVM, Multilayer Perceptron (MLP), and K-Nearest Neighbor (KNN). Early work in 2001 proposed a novel approach to bee species determination by focusing on the stable features of basal cells in wing images, detected through lines and intersections [20]. Utilizing Linear Discriminant Analysis (LDA), the method extracted key features to apply a deformable template specific to bee families, aiding in the identification of other wing cells. This process culminated in the use of SVM or Kernel Discriminant Analysis (KDA) to accurately recognize species based on these extracted features, demonstrating a sophisticated blend of image analysis and machine learning techniques for species determination. While previous publications focused on basal cell features in bee wings, reference [46] expanded the approach by utilizing both landmark-based features and associated images. These landmarks, located on wing veins, were automatically detected using TpsDig software(version 2.16). Figure 12 illustrates a sample of a wing of a honeybee showing the cells, veins, and landmarks. An Orthogonal Procrustes Analysis enabled the extraction of coordinates invariant to translation, scale, and rotation. The images were segmented into 256 quadrants, with the mean and standard deviation of each quadrant's blue channel serving as features. Among several shallow learning algorithms tested, the MLP showed superior performance. This study underscores the enhanced accuracy achieved by integrating image features with landmark information, outperforming methods relying solely on landmark features. [PERSON] et al. [47] adopted a landmark-based approach for bee species determination, examining seven combinations of feature selectors and classifiers. By analyzing 19 landmark positions and generating 84 additional landmark-based features, including centroid size, weight matrix, and relative warp scores, the study employed various feature selection methods--information gain, chi-square, correlation, and [PERSON]'s separation criterion--to refine the dataset. The research assessed classification efficacy using a variety of classifiers: LDA, Naive Bayes (NB), Logistic, KNN, C4.5, MLP, and SVM. It has been demonstrated that the NB classifier with correlation feature selector yielded the best performance, underscoring the crucial role of feature selection in improving performance. Despite its advances, the study did not surpass the performance previously reported in reference [46], highlighting the significant impact of integrating diverse wing feature data for more precise bee species determination. A fully automatic approach for bee species determination through wing image analysis was proposed [44], achieving an accuracy of up to 96% for species identification. It enhanced the classification accuracy by integrating color features with patterns derived from the wing's vein skeleton, employing the KNN algorithm for classification. The method significantly minimized manual intervention utilizing automated image processing tech  
Figure 12: Sample wing of a honeybee used for the species determination task. niques for feature extraction, showcasing the potential of combining different data types for robust classification. Two publications utilized SL techniques for the determination of bee species by basing their classification on the acoustic sounds produced by the bees. Flight sounds of bees and hornets were analyzed with ML for species determination purposes by [PERSON] and [PERSON] [12].