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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:

Image-based insect species and gender classification by trained supervised machine learning algorithms
Midori Tuda, Alejandro Isabel Luna-Maldonado
Ecological Informatics (2020) Vol. 60, pp. 101135-101135
Closed Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Deep learning approaches for challenging species and gender identification of mosquito vectors
Veerayuth Kittichai, Theerakamol Pengsakul, Kemmapon Chumchuen, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 70
A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance
Michael Tannous, Cesare Stefanini, Donato Romano
Insects (2023) Vol. 14, Iss. 2, pp. 148-148
Open Access | Times Cited: 30
Detection of Rice Pests Based on Self-Attention Mechanism and Multi-Scale Feature Fusion
Yuqi Hu, Xiaoling Deng, Yubin Lan, et al.
Insects (2023) Vol. 14, Iss. 3, pp. 280-280
Open Access | Times Cited: 28
Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors
Shuhan Lei, Jianbiao Luo, Xiaojun Tao, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4562-4562
Open Access | Times Cited: 37
Artificial Intelligence for Classification and Detection of Major Insect Pests of Brinjal
Niranjan Saikumar, N Emmanuel, Kalpesh Krishna, et al.
Indian Journal of Entomology (2023), pp. 563-566
Open Access | Times Cited: 14
Adaptive image processing embedding to make the ecological tasks of deep learning more robust on camera traps images
Zihe Yang, Ye Tian, Junguo Zhang
Ecological Informatics (2024) Vol. 82, pp. 102705-102705
Open Access | Times Cited: 4
AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis
Rui Miao, Haoyang Yu, Bing-Jie Zhong, et al.
PLoS Computational Biology (2025) Vol. 21, Iss. 2, pp. e1012773-e1012773
Open Access
AI-driven grain storage solutions: Exploring current technologies, applications, and future trends
T. Anukiruthika, Digvir S. Jayas
Journal of Stored Products Research (2025) Vol. 111, pp. 102588-102588
Open Access
Comparison of Deep Learning Techniques for Classification of the Insects in Order Level With Mobile Software Application
Durmuş Özdemir, Musa Selman Kunduracı
IEEE Access (2022) Vol. 10, pp. 35675-35684
Open Access | Times Cited: 20
Classification of Fruit Flies by Gender in Images Using Smartphones and the YOLOv4-Tiny Neural Network
М. А. Генаев, Е. Г. Комышев, Olga D. Shishkina, et al.
Mathematics (2022) Vol. 10, Iss. 3, pp. 295-295
Open Access | Times Cited: 19
An approach to multi-class imbalanced problem in ecology using machine learning
Bonelwa Sidumo, Energy Sonono, Isaac Takaidza
Ecological Informatics (2022) Vol. 71, pp. 101822-101822
Closed Access | Times Cited: 16
Getting the bugs out of AI: Advancing ecological research on arthropods through computer vision
Stefan Schneider, Graham W. Taylor, Stefan C. Kremer, et al.
Ecology Letters (2023) Vol. 26, Iss. 7, pp. 1247-1258
Open Access | Times Cited: 9
Cucurbitaceous family flower inferencing using deep transfer learning approaches: CuCuFlower UAV imagery data
M. Sivakumar, T. Y. J. Nagamalleswari
Soft Computing (2023) Vol. 27, Iss. 12, pp. 8345-8356
Closed Access | Times Cited: 7
Identification Method of Crop Aphids Based on Bionic Attention
Yue Hu, Zhiyong Li, Zhengyang Lu, et al.
Agronomy (2024) Vol. 14, Iss. 6, pp. 1093-1093
Open Access | Times Cited: 2
Gender identification of Drosophila melanogaster based on morphological analysis of microscopic images
L. Mestetskiy, D. S. Guru, J. V. Bibal Benifa, et al.
The Visual Computer (2022) Vol. 39, Iss. 5, pp. 1815-1827
Closed Access | Times Cited: 9
Automatic classification of parasitized fruit fly pupae from X-ray images by convolutional neural networks
Rangel Shida Marinho, Alysson Alexander Naves Silva, Clíssia Barboza da Silva, et al.
Ecological Informatics (2023) Vol. 78, pp. 102382-102382
Closed Access | Times Cited: 5
A survey on pest detection and classification in field crops using artificial intelligence techniques
P. Venkatasaichandrakanth, M. Iyapparaja
International Journal of Intelligent Robotics and Applications (2024) Vol. 8, Iss. 3, pp. 709-734
Closed Access | Times Cited: 1
Using honey bee flight activity data and a deep learning model as a toxicovigilance tool
Ulises Olivares‐Pinto, Cédric Alaux, Yves Le Conte, et al.
Ecological Informatics (2024) Vol. 81, pp. 102653-102653
Open Access | Times Cited: 1
Amphibian species detection in water reservoirs using artificial neural networks for ecology-friendly city planning
Zehra Karapιnar Şentürk
Ecological Informatics (2022) Vol. 69, pp. 101640-101640
Closed Access | Times Cited: 5
YOLOv8 Enables Automated Dragonfly Species Classification Using Wing Images
Su Yeon Kim
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Short communication: Insect detection using a machine learning model
Yash Munnalal Gupta, Somjit Homchan
Nusantara Bioscience (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 4
Avaliação de Redes Neurais e Proposta de uma Arquitetura de Comitê para Identificação de Pupas e Pragas em Sementes usando Imagens de Raios X
Alysson Alexander Naves Silva
(2024)
Open Access
Evaluation of Detection Accuracy of Image Recognition for Automatic Counting of Rice Planthoppers Captured on Sticky Boards
Tomohiko Takayama, Toshihisa Yashiro, Sachiyo Sanada, et al.
Agricultural Information Research (2021) Vol. 30, Iss. 4, pp. 174-184
Open Access | Times Cited: 3
Getting the Bugs Out: Entomology Using Computer Vision
Stefan Schneider, Graham Taylor, Stefan C. Kremer, et al.
Authorea (Authorea) (2022)
Open Access | Times Cited: 2
Coleoptera Classification Using Convolutional Neural Network and Transfer Learning
Jan Carlo T. Arroyo
International Journal of Engineering Trends and Technology (2021) Vol. 69, Iss. 5, pp. 1-5
Closed Access | Times Cited: 2
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