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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:

Assessing the potential for deep learning and computer vision to identify bumble bee species from images
Brian J. Spiesman, Claudio Gratton, Richard G. Hatfield, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 73

Showing 1-25 of 73 citing articles:

Emerging technologies revolutionise insect ecology and monitoring
Roel van Klink, Tom August, Yves Bas, et al.
Trends in Ecology & Evolution (2022) Vol. 37, Iss. 10, pp. 872-885
Open Access | Times Cited: 182
BacDive in 2022: the knowledge base for standardized bacterial and archaeal data
L.C. Reimer, J. Sarda Carbasse, Julia Koblitz, et al.
Nucleic Acids Research (2021) Vol. 50, Iss. D1, pp. D741-D746
Open Access | Times Cited: 157
Accurate detection and identification of insects from camera trap images with deep learning
Kim Bjerge, Jamie Alison, Mads Dyrmann, et al.
PLOS Sustainability and Transformation (2023) Vol. 2, Iss. 3, pp. e0000051-e0000051
Open Access | Times Cited: 67
Utilising affordable smartphones and open-source time-lapse photography for pollinator image collection and annotation
Valentin Ştefan, Aspen Workman, Jared C. Cobain, et al.
Journal of Pollination Ecology (2025) Vol. 37, pp. 1-21
Open Access | Times Cited: 2
Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes
Ryan M. Carney, Connor D. Mapes, Russanne Low, et al.
Insects (2022) Vol. 13, Iss. 8, pp. 675-675
Open Access | Times Cited: 43
Unravelling the use of artificial intelligence in management of insect pests
B. Kariyanna, M Sowjanya
Smart Agricultural Technology (2024) Vol. 8, pp. 100517-100517
Open Access | Times Cited: 16
Application of machine learning in automatic image identification of insects - a review
Yuanyi Gao, Xiaobao Xue, Guo‐Qing Qin, et al.
Ecological Informatics (2024) Vol. 80, pp. 102539-102539
Open Access | Times Cited: 15
Understanding and addressing shortfalls in European wild bee data
Leon Marshall, Nicolas Leclercq, Luísa G. Carvalheiro, et al.
Biological Conservation (2024) Vol. 290, pp. 110455-110455
Open Access | Times Cited: 14
Improving wild bee monitoring, sampling methods, and conservation
Felix Klaus, Manfred Ayasse, Alice Claßen, et al.
Basic and Applied Ecology (2024) Vol. 75, pp. 2-11
Open Access | Times Cited: 11
A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images
Felix Gregor Sauer, Moritz Werny, Kristopher Nolte, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Performance of Computer Vision Algorithms for Fine‐Grained Classification Using Crowdsourced Insect Images
Rita Pucci, Vincent J. Kalkman, Dan Stowell
IET Computer Vision (2025) Vol. 19, Iss. 1
Open Access | Times Cited: 1
Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning
Hjalte M. R. Mann, Alexandros Iosifidis, Jane Uhd Jepsen, et al.
Remote Sensing in Ecology and Conservation (2022) Vol. 8, Iss. 6, pp. 765-777
Open Access | Times Cited: 33
YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images
Thomas Stark, Valentin Ştefan, Michael Wurm, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 19
Exploring the landscape of automated species identification apps: Development, promise, and user appraisal
Minh-Xuân Truong, René van der Wal
BioScience (2024) Vol. 74, Iss. 9, pp. 601-613
Open Access | Times Cited: 7
Intensive monitoring for bees in North America: indispensable or improvident?
Vincent J. Tepedino, Zachary M. Portman
Insect Conservation and Diversity (2021) Vol. 14, Iss. 5, pp. 535-542
Closed Access | Times Cited: 39
Pursuing best practices for minimizing wild bee captures to support biological research
Ana Montero‐Castaño, Jonathan B. Koch, Thuy‐Tien Thai Lindsay, et al.
Conservation Science and Practice (2022) Vol. 4, Iss. 7
Open Access | Times Cited: 24
Image classification of sugarcane aphid density using deep convolutional neural networks
Ivan Grijalva, Brian J. Spiesman, Brian McCornack
Smart Agricultural Technology (2022) Vol. 3, pp. 100089-100089
Open Access | Times Cited: 23
Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database
Hatice Çatal Reis, Veysel Turk
Journal of Digital Imaging (2022) Vol. 36, Iss. 1, pp. 306-325
Open Access | Times Cited: 23
Detecting common coccinellids found in sorghum using deep learning models
Chaoxin Wang, Ivan Grijalva, Doina Caragea, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 13
Deep learning for identifying bee species from images of wings and pinned specimens
Brian J. Spiesman, Claudio Gratton, Elena M. Gratton, et al.
PLoS ONE (2024) Vol. 19, Iss. 5, pp. e0303383-e0303383
Open Access | Times Cited: 4
Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism
Xueyan Zhu, Xinwei Zhang, Zhao Sun, et al.
Forests (2021) Vol. 13, Iss. 1, pp. 1-1
Open Access | Times Cited: 25
A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models
Chu-Yuan Luo, Patrick Pearson, Guang Xu, et al.
Insects (2022) Vol. 13, Iss. 2, pp. 116-116
Open Access | Times Cited: 19
Wild Bee Nutritional Ecology: Integrative Strategies to Assess Foraging Preferences and Nutritional Requirements
Makaylee K. Crone, David J. Biddinger, Christina M. Grozinger
Frontiers in Sustainable Food Systems (2022) Vol. 6
Open Access | Times Cited: 17
Accuracy vs. Energy: An Assessment of Bee Object Inference in Videos from On-Hive Video Loggers with YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny
Vladimir Kulyukin, Aleksey V. Kulyukin
Sensors (2023) Vol. 23, Iss. 15, pp. 6791-6791
Open Access | Times Cited: 10
Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots
Jung-Il Kim, Jong-Won Baek, Chang-Bae Kim
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
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