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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:

Detecting common coccinellids found in sorghum using deep learning models
Chaoxin Wang, Ivan Grijalva, Doina Caragea, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 13

Showing 13 citing articles:

Rice-YOLO: An Automated Insect Monitoring in Rice Storage Warehouses with the Deep Learning Model
P. Vinass Jamali, V. Eyarkai Nambi, M. Loganathan, et al.
ACS Agricultural Science & Technology (2025)
Closed Access
YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery
Chenglei Sun, Afizan Azman, Zaiyun Wang, et al.
IEEE Access (2025) Vol. 13, pp. 19937-19945
Open Access
A review of deep learning applications in weed detection: UAV and robotic approaches for precision agriculture
Puneet Saini, D. S. Nagesh
European Journal of Agronomy (2025) Vol. 168, pp. 127652-127652
Closed Access
Precision Corn Pest Detection: Two-Step Transfer Learning for Beetles (Coleoptera) with MobileNet-SSD
E. Maican, A. Iosif, Sanda Maican
Agriculture (2023) Vol. 13, Iss. 12, pp. 2287-2287
Open Access | Times Cited: 9
Detecting and counting sorghum aphid alates using smart computer vision models
Ivan Grijalva, Haley Adams, N. A. Clark, et al.
Ecological Informatics (2024) Vol. 80, pp. 102540-102540
Open Access | Times Cited: 3
Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset
Guilherme Pires Silva de Almeida, Leonardo Nazário Silva dos Santos, Leandro Rodrigues da Silva Souza, et al.
Agronomy (2024) Vol. 14, Iss. 10, pp. 2194-2194
Open Access | Times Cited: 2
A novel dataset and deep learning object detection benchmark for grapevine pest surveillance
Giorgio Checola, Paolo Sonego, Roberto Zorer, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 2
Effect of image resolution on automatic detection of whitefly (Hemiptera: Aleyrodidae) species on tomato leaflets using deep learning
Mikio Kamei
Smart Agricultural Technology (2023) Vol. 6, pp. 100372-100372
Open Access | Times Cited: 3
Comprehensive wheat coccinellid detection dataset: Essential resource for digital entomology
Ivan Grijalva, N. A. Clark, Emma Hamilton, et al.
Data in Brief (2024) Vol. 55, pp. 110585-110585
Open Access
A Novel Dataset and Deep Learning Object Detection Benchmark for Grapevine Pest Surveillance
Giorgio Checola, Paolo Sonego, Roberto Zorer, et al.
(2024)
Closed Access
Deep Learning-Based Accurate Detection of Insects and Damage in Cruciferous Crops Using YOLOv5
Sourav Chakrabarty, P. R. Shashank, Chandan Kumar Deb, et al.
Smart Agricultural Technology (2024), pp. 100663-100663
Open Access
Pushing the boundaries of aphid detection: An investigation into mmWaveRadar and machine learning synergy
Liqiang Yuan, Fan Haozheng, Jing Xie, et al.
Computers and Electronics in Agriculture (2024) Vol. 229, pp. 109655-109655
Closed Access
Characterization study on eco-friendly break pad material using sorghum husk-derived Si3N4 and biochar friction modifier
E. Manoj, G. Selvakumar, Satya Prakash, et al.
Biomass Conversion and Biorefinery (2023) Vol. 14, Iss. 4, pp. 5735-5744
Closed Access
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