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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Species‐level image classification with convolutional neural network enables insect identification from habitus images
Oskar Liset Pryds Hansen, Jens‐Christian Svenning, Kent Olsen, et al.
Ecology and Evolution (2019) Vol. 10, Iss. 2, pp. 737-747
Open Access | Times Cited: 99

Showing 51-75 of 99 citing articles:

Bugs and bytes: Entomological biomonitoring through the integration of deep learning and molecular analysis for merged community and network analysis
Mukilan Deivarajan Suresh, Tong Xin, S. M. Cook, et al.
Agricultural and Forest Entomology (2024)
Open Access | Times Cited: 1
InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline
Shivani Chiranjeevi, Mojdeh Saadati, Zi K Deng, et al.
PNAS Nexus (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 1
Deep learning object detection to estimate the nectar sugar mass of flowering vegetation
D. M. Hicks, Mathilde Baude, Christoph C. Kratz, et al.
Ecological Solutions and Evidence (2021) Vol. 2, Iss. 3
Open Access | Times Cited: 10
Using deep neural networks to model similarity between visual patterns: Application to fish sexual signals
Samuel V. Hulse, Julien P. Renoult, Tamra C. Mendelson
Ecological Informatics (2021) Vol. 67, pp. 101486-101486
Open Access | Times Cited: 10
An ultra-specific image dataset for automated insect identification
Lakmini Abeywardhana, C. D. Dangalle, Anupiya Nugaliyadde, et al.
Multimedia Tools and Applications (2021) Vol. 81, Iss. 3, pp. 3223-3251
Closed Access | Times Cited: 10
Improving the Accuracy of Species Identification by Combining Deep Learning With Field Occurrence Records
Jianqiang Sun, Ryo Futahashi, Takehiko Yamanaka
Frontiers in Ecology and Evolution (2021) Vol. 9
Open Access | Times Cited: 10
Sharing taxonomic expertise between natural history collections using image recognition
Michael Greeff, Max Caspers, Vincent J. Kalkman, et al.
Research Ideas and Outcomes (2022) Vol. 8
Open Access | Times Cited: 7
Suivis de biodiversité par la reconnaissance automatique des espèces sur photographies : perspectives et défis
H. Le Borgne, Christophe Bouget
Naturae (2023), Iss. 6
Open Access | Times Cited: 3
The application of rapid evaporative ionization mass spectrometry in the analysis of Drosophila species—a potential new tool in entomology
Iris Wagner, Natalie Koch, Joscelyn Sarsby, et al.
Open Biology (2020) Vol. 10, Iss. 11
Open Access | Times Cited: 8
Machine Learning in Ethnobotany
Marc Böhlen, Wawan Sujarwo
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2020), pp. 108-113
Closed Access | Times Cited: 8
Machine-based mosquito taxonomy with a lightweight network-fused efficient dual ConvNet with residual learning and Knowledge Distillation
Francis Jesmar P. Montalbo
Applied Soft Computing (2022) Vol. 133, pp. 109913-109913
Closed Access | Times Cited: 5
Deep learning image analysis for filamentous fungi taxonomic classification: Dealing with small data sets with class imbalance and hierarchical grouping
Stefan Stiller, Juan F. Dueñas, Stefan Hempel, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
An ultra-specific image dataset for automated insect identification
Lakmini Abeywardhana, C. D. Dangalle, Anupiya Nugaliyadde, et al.
Multimedia Tools and Applications (2022)
Closed Access | Times Cited: 4
Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae
Kristian Peters, Birgitta König‐Ries
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 4
Taxonomic resolution of coral image classification with Convolutional Neural Network
B. Reshma, B. Rahul, K. R. Sreenath, et al.
Aquatic Ecology (2022) Vol. 57, Iss. 4, pp. 845-861
Closed Access | Times Cited: 4
Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision
Chun-yang LÜ, Shishuai Ge, Wei He, et al.
Journal of Integrative Agriculture (2022) Vol. 22, Iss. 7, pp. 2173-2187
Open Access | Times Cited: 3
Review of Classification and Detection for Insects/Pests Using Machine Learning and Deep Learning Approach
Sanjyot Thuse, Meena Chavan
Lecture notes in networks and systems (2024), pp. 167-182
Closed Access
Automated identification of aquatic insects: A case study using deep learning and computer vision techniques
Predrag Simović, Aleksandar Milosavljević, Katarina Stojanović, et al.
The Science of The Total Environment (2024) Vol. 935, pp. 172877-172877
Closed Access
From Pixels to Taxonomy: Advanced Insect Species Classification with EfficientNetB3 Transfer Learning
Rudresh Pillai, Neha Sharma, Deepak Upadhyay, et al.
(2024), pp. 1623-1628
Closed Access
Carabids hierarchical classification with ensemble model architecture
Gabin Moulie, Marie Beurton‐Aimar, Matthieu Valé
(2024), pp. 1-7
Closed Access
A deep learning approach to detect and visualise sexual dimorphism in monomorphic species
N. Silva, André C. Ferreira, Liliana R. Silva, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Neutrosophic Completed Local Binary Pattern Based Grasshopper Species Classification
Nuh Alpaslan, Mustafa İLÇİN
Tarım Bilimleri Dergisi (2024)
Open Access
Efficient Generation of Training Libraries for Image Classification Models from Photos of Herbarium Specimens
Alexander N. Schmidt‐Lebuhn, Nunzio Knerr
International Journal of Plant Sciences (2023) Vol. 184, Iss. 5, pp. 387-391
Closed Access | Times Cited: 1
Multi-class Classification of Insects using Deep Neural Networks
M. Santhiya, A Priyadharshini, J Agshalal Sheeba, et al.
2022 International Conference on Computer Communication and Informatics (ICCCI) (2023), pp. 1-5
Closed Access | Times Cited: 1
Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula
Jack D. Hollister, Xiaohao Cai, Tammy Horton, et al.
Frontiers in Marine Science (2023) Vol. 10
Open Access | Times Cited: 1

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