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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:

Detection and Identification of Fish Skin Health Status Referring to Four Common Diseases Based on Improved YOLOv4 Model
Gangyi Yu, Junbo Zhang, Ao Chen, et al.
Fishes (2023) Vol. 8, Iss. 4, pp. 186-186
Open Access | Times Cited: 13

Showing 13 citing articles:

FireViTNet: A hybrid model integrating ViT and CNNs for forest fire segmentation
Guodong Wang, Di Bai, Haifeng Lin, et al.
Computers and Electronics in Agriculture (2024) Vol. 218, pp. 108722-108722
Closed Access | Times Cited: 8
DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments
Yinjia Li, Zeyuan Hu, Yixi Zhang, et al.
Fishes (2024) Vol. 9, Iss. 6, pp. 242-242
Open Access | Times Cited: 8
Machine Vision Applications for Welfare Monitoring in Aquaculture: Challenges and Opportunities
A Fitzgerald, Christos C. Ioannou, Sofía Consuegra, et al.
Aquaculture Fish and Fisheries (2025) Vol. 5, Iss. 1
Open Access
Fish Surface Damage Detection with Improved YOLOv7
Hanchi Liu, Haoran Li, Xin Ma, et al.
Lecture notes in computer science (2025), pp. 157-168
Closed Access
AI-driven aquaculture: A review of technological innovations and their sustainable impacts
Hang Yang, Feng Qi, Shibin Xia, et al.
Artificial Intelligence in Agriculture (2025)
Open Access
Real-time detection and identification of fish skin health in the underwater environment based on improved YOLOv10 model
Duanrui Wang, Meng Wu, Xingyue Zhu, et al.
Aquaculture Reports (2025) Vol. 42, pp. 102723-102723
Closed Access
An efficient detection model based on improved YOLOv5s for abnormal surface features of fish
Zheng Zhang, Xiang Lu, Shouqi Cao
Mathematical Biosciences & Engineering (2024) Vol. 21, Iss. 2, pp. 1765-1790
Open Access | Times Cited: 3
Intelligent recognition and behavior tracking of sea cucumber infected with Vibrio alginolyticus based on machine vision
Wenkai Xu, Peidong Wang, Lingxu Jiang, et al.
Aquacultural Engineering (2023) Vol. 103, pp. 102368-102368
Closed Access | Times Cited: 5
Real-Time Monitoring of Aquarium Conditions to Ensure Optimal Fish Health
Asha Rani Borah, Manik Gupta, Sudeep M Arashingodi
(2024), pp. 1555-1559
Closed Access
Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations
Haobijam Basanta, Yo‐Ping Huang, Yue‐Shan Chang, et al.
International Journal of Fuzzy Systems (2024)
Closed Access
Empirical Evaluation of Deep Learning Techniques for Fish Disease Detection in Aquaculture Systems: A Transfer Learning and Fusion-Based Approach
Subir Biswas, Debendra Muduli, Md Ariful Islam, et al.
IEEE Access (2024) Vol. 12, pp. 176136-176154
Open Access
Deep Learning for Early Detection of Disease Outbreaks
S.M.S. Chahal
International Journal of Science and Research (IJSR) (2022) Vol. 11, Iss. 11, pp. 1489-1495
Open Access | Times Cited: 2
Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization
Shuai Yang, Lin Zhou, Chen Wang, et al.
Research Square (Research Square) (2023)
Open Access
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