ArticleInsects2022
Using the Software DeepWings© to Classify Honey Bees across Europe through Wing Geometric Morphometrics.
Article in Insects, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Country specific hybridization of honey bees from lineage M.GigaScience · 2026Article
- An XGBoost-Based Morphometric Classification System for Automatic Subspecies Identification ofInsects · 2025Article
- Variation of honey bee wings in southwestern Asia.Scientific data · 2025Article
- Automated Honey Bee Subspecies Identification Using Advanced Wing Venation Analysis and Adaptive Hierarchical Clustering.Ecology and evolution · 2025Article
- Image-based honey bee larval viral and bacterial diagnosis using machine learning.Scientific reports · 2025Article
- Comparing Morphometric and Mitochondrial DNA Data from Honeybees and Honey Samples for IdentifyingAnimals : an open access journal from MDPI · 2025Article
- Conservation ofInsects · 2025Review
- Article
- Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It.Insects · 2024Review
- In Search ofLife (Basel, Switzerland) · 2023Article
- Conservation of Local Honeybees (Animals : an open access journal from MDPI · 2023Article
- Article
- Honey bee (Apis mellifera) wing images: a tool for identification and conservation.GigaScience · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
DeepWings© is a software that uses machine learning to automatically classify honey bee subspecies by wing geometric morphometrics. Here, we tested the five subspecies classifier (A. m. carnica, Apis mellifera caucasia, A. m. iberiensis, Apis mellifera ligustica, and A. m. mellifera) of DeepWings© on 14,816 wing images with variable quality and acquired by different beekeepers and researchers. These images represented 2601 colonies from the native ranges of the M-lineage A. m. iberiensis and A. m. mellifera, and the C-lineage A. m. carnica. In the A. m. iberiensis range, 92.6% of the colonies matched this subspecies, with a high median probability (0.919). In the Azores, where the Iberian subspecies was historically introduced, a lower proportion (85.7%) and probability (0.842) were observed. In the A. m mellifera range, only 41.1 % of the colonies matched this subspecies, which is compatible with a history of C-derived introgression. Yet, these colonies were classified with the highest probability (0.994) of the three subspecies. In the A. m. carnica range, 88.3% of the colonies matched this subspecies, with a probability of 0.984. The association between wing and molecular markers, assessed for 1214 colonies from the M-lineage range, was highly significant but not strong (r = 0.31, p < 0.0001). The agreement between the markers was influenced by C-derived introgression, with the best results obtained for colonies with high genetic integrity. This study indicates the good performance of DeepWings© on a realistic wing image dataset.
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