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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:

InsectCV: A system for insect detection in the lab from trap images
Telmo De Cesaro Júnior, Rafael Rieder, Jéssica Regina Di Domênico, et al.
Ecological Informatics (2021) Vol. 67, pp. 101516-101516
Open Access | Times Cited: 22

Showing 22 citing articles:

Artificial Intelligence: Implications for the Agri-Food Sector
Akriti Taneja, Gayathri Nair, Manisha Joshi, et al.
Agronomy (2023) Vol. 13, Iss. 5, pp. 1397-1397
Open Access | Times Cited: 91
Recognizing Wheat Aphid Disease Using a Novel Parallel Real-Time Technique Based on Mask Scoring RCNN
Vinay Kukreja, Deepak Kumar, Ankit Bansal, et al.
2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (2022), pp. 1372-1377
Closed Access | Times Cited: 59
A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton
Denis Olgen Kiobia, Canicius Mwitta, Kadeghe G. Fue, et al.
Sensors (2023) Vol. 23, Iss. 8, pp. 4127-4127
Open Access | Times Cited: 28
Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications
Jun Wang, Yanlong Wang, Guang Li, et al.
Agronomy (2024) Vol. 14, Iss. 9, pp. 1975-1975
Open Access | Times Cited: 9
Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting
Changji Wen, Hongrui Chen, Zhenyu Ma, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 37
From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring
Philipp Batz, Torsten Will, Steffen Thiel, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 21
Emerging technologies for fast determination of nutritional quality and safety of insects for food and feed: A review
Frank Ssemakula, Sarah Nawoya, Catherine Nkirote Kunyanga, et al.
(2025)
Closed Access
Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D
Béla J. Szekeres, Natabara Máté Gyöngyössy, János Botzheim
Ecological Informatics (2025) Vol. 86, pp. 103055-103055
Open Access
Deep learning-based detection of aphid colonies on plants from a reconstructed Brassica image dataset
Abderraouf Amrani, Ferdous Sohel, Dean Diepeveen, et al.
Computers and Electronics in Agriculture (2022) Vol. 205, pp. 107587-107587
Closed Access | Times Cited: 19
Developing a hybrid convolutional neural network for automatic aphid counting in sugar beet fields
Xumin Gao, Wenxin Xue, Callum Lennox, et al.
Computers and Electronics in Agriculture (2024) Vol. 220, pp. 108910-108910
Open Access | Times Cited: 4
New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review
Dan Popescu, Alexandru Dinca, Loretta Ichim, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 10
Bugs and Bytes: Entomological Biomonitoring in the Age of Deep Learning and Beyond
Mukilan Deivarajan Suresh, Tong Xin, Darren M. Evans, et al.
(2024)
Open Access | Times Cited: 3
STARdbi: A pipeline and database for insect monitoring based on automated image analysis
Tamar Keasar, Michael Yair, Daphna Gottlieb, et al.
Ecological Informatics (2024) Vol. 80, pp. 102521-102521
Open Access | Times Cited: 3
Automatic classification of parasitized fruit fly pupae from X-ray images by convolutional neural networks
Rangel Shida Marinho, Alysson Alexander Naves Silva, Clíssia Barboza da Silva, et al.
Ecological Informatics (2023) Vol. 78, pp. 102382-102382
Closed Access | Times Cited: 5
MRISVM: A Object Detection and Feature Vector Machine Based Network for Brown Mite Variation in Wheat Plant
Deepak Kumar, Vinay Kukreja
2021 International Conference on Data Analytics for Business and Industry (ICDABI) (2022), pp. 707-711
Closed Access | Times Cited: 3
A Novel Computer Vision System for Efficient Flea Beetle Monitoring in Canola Crop
Muhib Ullah, Mohammad Alizadeh Hasan, Abdul Bais, et al.
IEEE Transactions on AgriFood Electronics (2024) Vol. 2, Iss. 2, pp. 483-496
Closed Access
Bugs and bytes: Entomological biomonitoring through the integration of deep learning and molecular analysis for merged community and network analysis
Mukilan Deivarajan Suresh, Tong Xin, S. M. Cook, et al.
Agricultural and Forest Entomology (2024)
Open Access
Desenvolvimento de um Modelo Inteligente para Identificar e Contar Insetos em Armadilhas Adesivas
Artur Silva, Ana Beatriz Guimarães Vasconcelos Rosa, Nicolas Welfer Kirinus, et al.
(2024), pp. 175-178
Closed Access
Signal, Image Processing and Embedded Systems Trends

(2022)
Open Access | Times Cited: 1
A Smart Trap for Counting Olive Moths Based on the Internet of Things and Deep Learning
Afef Mdhaffar, Bechir Zalila, Racem Moalla, et al.
(2022)
Closed Access | Times Cited: 1
Artificial Intelligence and Computational Vision in Innovative Enterprises Projects: Analysis of theTecnova and Centelha Programs in the State of Alagoas
Bruno Vicente Nunes de Oliveira, Luciana Peixoto Santa Rita, Araken Alves de Lima
Diversitas Journal (2023) Vol. 8, Iss. 2, pp. 896-911
Open Access
A Systematic Literature Review on Insect Detection in Images
Lotfi Souifi, Afef Mdhaffar, Ismael Bouassida Rodriguez, et al.
(2022), pp. 41-56
Open Access
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