ArticleScientific reports2021
Assessing the potential for deep learning and computer vision to identify bumble bee species from images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
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- Computer Vision for Monitoring Wild Bees and Wasps: A Structured Literature Review.Ecology and evolution · 2026Review
- Insect education and outreach: integrating traditional and non-lethal collection techniques.Journal of insect science (Online) · 2026Review
- Hyperparameter optimization to enhance the performance of deep learning models for the early detection of invasive turtles in Korea.Scientific reports · 2026Article
- Clinical benefits of deep learning-assisted ultrasound in predicting lymph node metastasis in pancreatic cancer patients.Future oncology (London, England) · 2025Article
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review.Biology · 2025Review
- Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps.Pest management science · 2025Article
- Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.Scientific reports · 2025Article
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- Evaluating the method reproducibility of deep learning models in biodiversity research.PeerJ. Computer science · 2025Article
- InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline.PNAS nexus · 2025Article
- Utilizing CNNs for classification and uncertainty quantification for 15 families of European fly pollinators.PloS one · 2025Article
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- Article
- Exploring the landscape of automated species identification apps: Development, promise, and user appraisal.Bioscience · 2024Review
- A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images.Scientific reports · 2024Article
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- Deep learning-based image classification of sea turtles using object detection and instance segmentation models.PloS one · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.
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