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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 detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep
Jasmeet Kaler, Jürgen Mitsch, Jorge A. Vázquez-Diosdado, et al.
Royal Society Open Science (2020) Vol. 7, Iss. 1, pp. 190824-190824
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Machine Learning in Agriculture: A Comprehensive Updated Review
Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, et al.
Sensors (2021) Vol. 21, Iss. 11, pp. 3758-3758
Open Access | Times Cited: 526
Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare
Juliette Schillings, Richard Bennett, David Christian Rose
Frontiers in Animal Science (2021) Vol. 2
Open Access | Times Cited: 109
Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges
Parisa Niloofar, Deena P. Francis, Sanja Lazarova‐Molnar, et al.
Computers and Electronics in Agriculture (2021) Vol. 190, pp. 106406-106406
Open Access | Times Cited: 89
A survey of machine learning approaches in animal behaviour
Natasa Kleanthous, Abir Hussain, Wasiq Khan, et al.
Neurocomputing (2022) Vol. 491, pp. 442-463
Open Access | Times Cited: 39
Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data
Victor Bloch, Lilli Frondelius, Claudia Arcidiacono, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2611-2611
Open Access | Times Cited: 25
Affective State Recognition in Livestock—Artificial Intelligence Approaches
Suresh Neethirajan
Animals (2022) Vol. 12, Iss. 6, pp. 759-759
Open Access | Times Cited: 34
Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review
Carlos Alberto Aguilar-Lazcano, Ismael Edrein Espinosa‐Curiel, Jorge Ríos-Martínez, et al.
Sensors (2023) Vol. 23, Iss. 12, pp. 5732-5732
Open Access | Times Cited: 18
Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock
Emily Price, Joss Langford, Tim W. Fawcett, et al.
Applied Animal Behaviour Science (2022) Vol. 251, pp. 105630-105630
Closed Access | Times Cited: 22
Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review
Hesameddin Akbarein, Mohammad Hussein Taaghi, Mohammad Reza Mohebbi, et al.
Veterinary Medicine and Science (2025) Vol. 11, Iss. 3
Open Access
Digital Sentience? Evaluating the Integration of AI‐Driven Tools in Animal Welfare Assessment
Sara Platto
Animal Research and One Health (2025)
Open Access
Do we automatically detect health- or general welfare-related issues? A framework
Joanna Stachowicz, Christina Umstätter
Proceedings of the Royal Society B Biological Sciences (2021) Vol. 288, Iss. 1950
Open Access | Times Cited: 22
Video dataset of sheep activity for animal behavioral analysis via deep learning
Nathan A. Kelly, Bilal Khan, Muhammad Yaseen Ayub, et al.
Data in Brief (2024) Vol. 52, pp. 110027-110027
Open Access | Times Cited: 3
Lameness Detection in Cows Using Hierarchical Deep Learning and Synchrosqueezed Wavelet Transform
Delaram Jarchi, Jasmeet Kaler, Saeid Sanei
IEEE Sensors Journal (2021) Vol. 21, Iss. 7, pp. 9349-9358
Open Access | Times Cited: 19
What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture
Seer J. Ikurior, Nelly Marquetoux, Stephan T. Leu, et al.
Sensors (2021) Vol. 21, Iss. 20, pp. 6816-6816
Open Access | Times Cited: 19
Wearable Inertial Sensor-Based Limb Lameness Detection and Pose Estimation for Horses
Tarik Yigit, Feng Han, Ellen M. Rankins, et al.
IEEE Transactions on Automation Science and Engineering (2022) Vol. 19, Iss. 3, pp. 1365-1379
Open Access | Times Cited: 13
Computer Vision for Detection of Body Posture and Behavior of Red Foxes
Anne K. Schütz, Eike Krause, Mareike Fischer, et al.
Animals (2022) Vol. 12, Iss. 3, pp. 233-233
Open Access | Times Cited: 12
Affective State Recognition in Livestock – Artificial Intelligence Approaches
Suresh Neethirajan
(2022)
Open Access | Times Cited: 11
Evaluation of experimental error in accelerometer monitoring: Variation among individual animals versus variation among devices
Colin Tobin, Derek W. Bailey, Caroline Wade, et al.
Smart Agricultural Technology (2024) Vol. 7, pp. 100432-100432
Open Access | Times Cited: 2
hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
Ci Li, Nima Ghorbani, Sofia Broomé, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 12
Applying Different Resampling Strategies In Random Forest Algorithm To Predict Lumpy Skin Disease
Suparyati Suparyati, Emma Utami, Alva Hendi Muhammad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (2022) Vol. 6, Iss. 4, pp. 555-562
Open Access | Times Cited: 9
Wearable IMU-based Early Limb Lameness Detection for Horses using Multi-Layer Classifiers
Tarik Yigit, Feng Han, Ellen M. Rankins, et al.
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (2020), pp. 955-960
Closed Access | Times Cited: 11
A Review of Sensors and Machine Learning in Animal Farming
Ahmed Yaseer, Heping Chen
(2021), pp. 747-752
Closed Access | Times Cited: 9
Machine learning algorithms for lamb survival
Bahadir Baran Odevci, Ebru Emsen, Mehmet N. Aydın
Computers and Electronics in Agriculture (2021) Vol. 182, pp. 105995-105995
Closed Access | Times Cited: 7
Sheep's coping style can be identified by unsupervised machine learning from unlabeled data
Cihan Çakmakçı
Behavioural Processes (2021) Vol. 194, pp. 104559-104559
Closed Access | Times Cited: 7
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