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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:

Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish
Wenkai Xu, Zhaohu Zhu, Fengli Ge, et al.
Sensors (2020) Vol. 20, Iss. 16, pp. 4425-4425
Open Access | Times Cited: 48

Showing 1-25 of 48 citing articles:

Detection and classification of tea buds based on deep learning
Wenkai Xu, Longgang Zhao, Juan Li, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106547-106547
Closed Access | Times Cited: 125
A novel method for peanut variety identification and classification by Improved VGG16
Haoyan Yang, Jiangong Ni, Jiyue Gao, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 109
Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model
Haiqing Wang, Shuqi Shang, Dongwei Wang, et al.
Agriculture (2022) Vol. 12, Iss. 7, pp. 931-931
Open Access | Times Cited: 77
Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish
Daoliang Li, Ling Du
Artificial Intelligence Review (2021) Vol. 55, Iss. 5, pp. 4077-4116
Closed Access | Times Cited: 92
A lightweight dead fish detection method based on deformable convolution and YOLOV4
Shili Zhao, Song Zhang, Jiamin Lu, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107098-107098
Closed Access | Times Cited: 69
Deep learning for visual recognition and detection of aquatic animals: A review
Juan Li, Wenkai Xu, Limiao Deng, et al.
Reviews in Aquaculture (2022) Vol. 15, Iss. 2, pp. 409-433
Closed Access | Times Cited: 65
A Review on the Use of Computer Vision and Artificial Intelligence for Fish Recognition, Monitoring, and Management
Jayme Garcia Arnal Barbedo
Fishes (2022) Vol. 7, Iss. 6, pp. 335-335
Open Access | Times Cited: 46
Recognition and counting of typical apple pests based on deep learning
Tiewei Wang, Longgang Zhao, Baohua Li, et al.
Ecological Informatics (2022) Vol. 68, pp. 101556-101556
Closed Access | Times Cited: 45
Broodstock breeding behaviour recognition based on Resnet50-LSTM with CBAM attention mechanism
Ling Du, Zhaocheng Lu, Daoliang Li
Computers and Electronics in Agriculture (2022) Vol. 202, pp. 107404-107404
Closed Access | Times Cited: 39
Application of Deep Learning-Based Object Detection Techniques in Fish Aquaculture: A Review
Hanchi Liu, Xin Ma, Yining Yu, et al.
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 4, pp. 867-867
Open Access | Times Cited: 37
Research progress of computer vision technology in abnormal fish detection
Chunhong Liu, Zhiyong Wang, Yachao Li, et al.
Aquacultural Engineering (2023) Vol. 103, pp. 102350-102350
Closed Access | Times Cited: 27
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 | Times Cited: 1
AquaYOLO: Advanced YOLO-based fish detection for optimized aquaculture pond monitoring
M. Vijayalakshmi, A. Sasithradevi
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Recent advances of target tracking applications in aquaculture with emphasis on fish
Yupeng Mei, Boyang Sun, Daoliang Li, et al.
Computers and Electronics in Agriculture (2022) Vol. 201, pp. 107335-107335
Closed Access | Times Cited: 33
Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm
Ari Kuswantori, T. Suesut, Worapong Tangsrirat, et al.
Applied Sciences (2023) Vol. 13, Iss. 6, pp. 3812-3812
Open Access | Times Cited: 19
The effectiveness of activated carbon from nutmeg shell in reducing ammonia (NH3) levels in fish pond water
Muhammadin Hamid, Syahrul Humaidi, Indah Revita Saragi, et al.
Carbon Trends (2024) Vol. 14, pp. 100324-100324
Open Access | Times Cited: 7
The impact of ammonia and microcystin-LR on neurobehavior and glutamate/gamma-aminobutyric acid balance in female zebrafish (Danio rerio): ROS and inflammation as key pathways
Ya He, Kang Ou-Yang, Hui Yang, et al.
The Science of The Total Environment (2024) Vol. 920, pp. 170914-170914
Closed Access | Times Cited: 7
EResNet‐SVM: an overfitting‐relieved deep learning model for recognition of plant diseases and pests
Haitao Xiong, Juan Li, Tiewei Wang, et al.
Journal of the Science of Food and Agriculture (2024) Vol. 104, Iss. 10, pp. 6018-6034
Closed Access | Times Cited: 7
Behavioral response of fish under ammonia nitrogen stress based on machine vision
Wenkai Xu, Chang Liu, Guangxu Wang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 128, pp. 107442-107442
Closed Access | Times Cited: 13
AD-YOLOv5: An object detection approach for key parts of sika deer based on deep learning
Haitao Xiong, Ying Xiao, Zhao Hai-ping, et al.
Computers and Electronics in Agriculture (2024) Vol. 217, pp. 108610-108610
Closed Access | Times Cited: 5
A novel automatic detection method for breeding behavior of broodstock based on improved YOLOv5
Ling Du, Zhaocheng Lu, Daoliang Li
Computers and Electronics in Agriculture (2023) Vol. 206, pp. 107639-107639
Closed Access | Times Cited: 12
Automated Prediction of Spawning Nights Using Machine Learning Analysis of Flatfish Behaviour
Abdul Qadir, Neil Duncan, Wendy Ángela González-López, et al.
(2025)
Closed Access
A new quantitative analysis method for the fish behavior under ammonia nitrogen stress based on pruning strategy
Wenkai Xu, Jiaxuan Yu, Ying Xiao, et al.
Aquaculture (2025), pp. 742192-742192
Closed Access
An Analysis of the Movement Trajectories of the Endangered Acipenser gueldenstaedtii in Ammonia-Supplemented Environments Using Image Processing Methods
Beytullah Ahmet Balcı, Güray Tonguç, Muhammed Nurullah Arslan, et al.
Animals (2025) Vol. 15, Iss. 7, pp. 900-900
Open Access
Nonintrusive and automatic quantitative analysis methods for fish behaviour in aquaculture
Jintao Liu, Fernando Bienvenido, Xinting Yang, et al.
Aquaculture Research (2022) Vol. 53, Iss. 8, pp. 2985-3000
Closed Access | Times Cited: 13
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