ArticleProceedings of the National Academy of Sciences of the United States of America2021
Deep learning and computer vision will transform entomology.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 1 of them a synthesis that pooled it.
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Who cites it
111 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
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- Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds.Journal of insect science (Online) · 2026Article
- Multi-angle, cross-domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae).Cladistics : the international journal of the Willi Hennig Society · 2026Article
- Computer Vision for Monitoring Wild Bees and Wasps: A Structured Literature Review.Ecology and evolution · 2026Review
- Unraveling hidden species diversity of talpid moles using phylogenomics and skull-based deep learning.Communications biology · 2026Article
- The spread of non-native species.Biological reviews of the Cambridge Philosophical Society · 2026Review
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- Continuous Non-Invasive Monitoring of Hive Entrance Activity Reveals Honey Bee Colony Dynamics.Biology · 2026Article
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- Harnessing mmWave signals and machine learning for noninvasive taxonomic classification of insects.PNAS nexus · 2026Article
- AntID_APP: Empowering Citizen Scientists with YOLO Models for Ant Identification in Taiwan.Biology · 2026Article
- Optimizing Vibratory Sorting Machine of Crickets: Effects of Surface Friction, Oscillation Dynamics, and Energy Consumption.Insects · 2026Article
- Accuracy of occurrence and abundance estimates from insect metabarcoding.bioRxiv : the preprint server for biology · 2026Article
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture.Scientific reports · 2026Article
- MVBeetle: an interpretable multi-view deep learning model for fine-grained classification of Galerucinae and Alticinae (Coleoptera: Chrysomelidae).Frontiers in plant science · 2026Article
- Article
51 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.
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