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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:

Automated monitoring and analyses of honey bee pollen foraging behavior using a deep learning-based imaging system
Thi Nha Ngo, Dan Jeric Arcega Rustia, En‐Cheng Yang, et al.
Computers and Electronics in Agriculture (2021) Vol. 187, pp. 106239-106239
Closed Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services
Hugo Hadjur, Doreid Ammar, Laurent Lefèvre
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106604-106604
Open Access | Times Cited: 64
A framework for better sensor-based beehive health monitoring
Asaduz Zaman, Alan Dorin
Computers and Electronics in Agriculture (2023) Vol. 210, pp. 107906-107906
Open Access | Times Cited: 19
Artificial intelligence-based classification of pollen grains using attention-guided pollen features aggregation network
Tahir Mahmood, Jiho Choi, Kang Ryoung Park
Journal of King Saud University - Computer and Information Sciences (2023) Vol. 35, Iss. 2, pp. 740-756
Open Access | Times Cited: 15
Precision Beekeeping Systems: State of the Art, Pros and Cons, and Their Application as Tools for Advancing the Beekeeping Sector
Pier Paolo Danieli, Nicola Francesco Addeo, Filippo Lazzari, et al.
Animals (2023) Vol. 14, Iss. 1, pp. 70-70
Open Access | Times Cited: 15
AI in apiculture: A novel framework for recognition of invasive insects under unconstrained flying conditions for smart beehives
Abdul Nasir, Muhammad Obaid Ullah, Muhammad Haroon Yousaf
Engineering Applications of Artificial Intelligence (2023) Vol. 119, pp. 105784-105784
Closed Access | Times Cited: 14
Machine learning and computer vision techniques in continuous beehive monitoring applications: A survey
Šimon Bilík, Tomáš Zemčík, Lukáš Kratochvíla, et al.
Computers and Electronics in Agriculture (2023) Vol. 217, pp. 108560-108560
Open Access | Times Cited: 13
Improving pollen-bearing honey bee detection from videos captured at hive entrance by combining deep learning and handling imbalance techniques
Dinh-Tu Nguyen, Thi-Nhung Le, Thi-Huong Phung, et al.
Ecological Informatics (2024) Vol. 82, pp. 102744-102744
Open Access | Times Cited: 4
IoT and Machine Learning Techniques for Precision Beekeeping: A Review
Agatha Turyagyenda, Andrew Katumba, Roseline Akol, et al.
AI (2025) Vol. 6, Iss. 2, pp. 26-26
Open Access
Visual recognition of honeybee behavior patterns at the hive entrance
Tomyslav Sledevič, Artūras Serackis, Dalius Matuzevičius, et al.
PLoS ONE (2025) Vol. 20, Iss. 2, pp. e0318401-e0318401
Open Access
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
Is the Artificial Pollination of Walnut Trees with Drones Able to Minimize the Presence of <em>Xanthomonas arboricola </em>pv. juglandis? Review.
Ioannis Manthos, Thomas Sotiropoulos, Ioannis Vagelas
(2024)
Open Access | Times Cited: 3
Is the Artificial Pollination of Walnut Trees with Drones Able to Minimize the Presence of Xanthomonas arboricola pv. juglandis? A Review
Ioannis Manthos, Thomas Sotiropoulos, Ioannis Vagelas
Applied Sciences (2024) Vol. 14, Iss. 7, pp. 2732-2732
Open Access | Times Cited: 3
Honeybee pollen but not nectar foraging greatly reduced by neonicotinoids: Insights from AI and simulation
Ming Wang, Frederic Tausch, Katharina Schmidt, et al.
Computers and Electronics in Agriculture (2024) Vol. 221, pp. 108966-108966
Open Access | Times Cited: 3
Monitoring System Enhancing the Potential of Urban Beekeeping
Andrzej Szczurek, Monika Maciejewska, Piotr Batog
Applied Sciences (2023) Vol. 13, Iss. 1, pp. 597-597
Open Access | Times Cited: 8
Prediction of the remaining time of the foraging activity of honey bees using spatio-temporal correction and periodic model re-fitting
Paweł Majewski, Piotr Lampa, Robert Burduk, et al.
Computers and Electronics in Agriculture (2023) Vol. 205, pp. 107596-107596
Closed Access | Times Cited: 7
Climatic fluctuations alter the preference of stingless bees (Apidae, Meliponini) towards food contaminated with acephate and glyphosate
Lívia Maria Negrini Ferreira, Michael Hrncir, Danilo Almeida, et al.
The Science of The Total Environment (2024) Vol. 952, pp. 175892-175892
Closed Access | Times Cited: 2
DISEASE DETECTION FROM CASSAVA LEAF IMAGES WITH DEEP LEARNING METHODS IN WEB ENVIRONMENT
Sedat Metlek
International Journal of 3D Printing Technologies and Digital Industry (2021) Vol. 5, Iss. 3, pp. 625-644
Open Access | Times Cited: 13
Toward Bee Behavioral Pattern Recognition on Hive Entrance using YOLOv8
Tomyslav Sledevič, Darius Plonis
(2023), pp. 1-4
Closed Access | Times Cited: 5
Foraging flight-based health indicators for honey bee colonies using automatic monitoring systems
Joe‐Air Jiang, Jen-Cheng Wang, Chien-Peng Huang, et al.
Computers and Electronics in Agriculture (2023) Vol. 216, pp. 108476-108476
Closed Access | Times Cited: 5
Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques
Jose Divasón, Ana Romero, Francisco Javier Martínez-de-Pisón, et al.
Sensors (2024) Vol. 24, Iss. 12, pp. 3828-3828
Open Access | Times Cited: 1
PollenNet: A Novel Deep Learning Architecture for High Precision Pollen Grain Classification through Deep Learning and Explainable AI
F M Javed Mehedi Shamrat, Mohd Yamani Idna Idris, Xujuan Zhou, et al.
Heliyon (2024) Vol. 10, Iss. 19, pp. e38596-e38596
Open Access | Times Cited: 1
FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive
Tomyslav Sledevič, Artūras Serackis, Darius Plonis
Agriculture (2022) Vol. 12, Iss. 11, pp. 1849-1849
Open Access | Times Cited: 7
Varroa Mite Detection Using Deep Learning Techniques
Jose Divasón, Francisco Javier Martínez-de-Pisón, Ana Romero, et al.
Lecture notes in computer science (2023), pp. 326-337
Closed Access | Times Cited: 3
Vibration inducing and airflow guiding coupled tomato pollination method research based on gas–solid two-phase flow model
Siyao Liu, Xuemin Zhang, Xiaoyan Wang, et al.
Computers and Electronics in Agriculture (2023) Vol. 216, pp. 108472-108472
Closed Access | Times Cited: 3
Marmara Adasında Bulunan Bal Arılarının (Apis mellifera L.) Polen Tercihleri
Omer SOLAK AMET, İbrahim Çakmak, Aycan Tosunoğlu
Journal of Apitherapy and Nature (2023) Vol. 6, Iss. 2, pp. 41-56
Open Access | Times Cited: 3
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