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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:

NemaNet: A convolutional neural network model for identification of soybean nematodes
André Abade, Lucas Faria Porto, Paulo Afonso Ferreira, et al.
Biosystems Engineering (2021) Vol. 213, pp. 39-62
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Object detection and tracking in Precision Farming: a systematic review
Mar Ariza-Sentís, Sergio Vélez, Raquel Martínez‐Peña, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108757-108757
Open Access | Times Cited: 36
Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review
Yahong Wang, Wen‐Hao Su
Agronomy (2022) Vol. 12, Iss. 11, pp. 2659-2659
Open Access | Times Cited: 59
Soybean cyst nematode detection and management: a review
Youness Arjoune, Niroop Sugunaraj, Sai Peri, et al.
Plant Methods (2022) Vol. 18, Iss. 1
Open Access | Times Cited: 44
Deep learning models for automatic identification of plant-parasitic nematode
Nabila Husna Shabrina, Ryukin Aranta Lika, Siwi Indarti
Artificial Intelligence in Agriculture (2023) Vol. 7, pp. 1-12
Open Access | Times Cited: 24
Current advances in the identification of plant nematode diseases: From lab assays to in-field diagnostics
Hudie Shao, Pan Zhang, Deliang Peng, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 24
Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review
Mayuri Sharma, Chandan Jyoti Kumar, Dhruba K. Bhattacharyya
Biosystems Engineering (2024) Vol. 244, pp. 77-92
Closed Access | Times Cited: 11
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: 7
Detection and counting of root-knot nematodes using YOLO models with mosaic augmentation
Top Bahadur Pun, Arjun Neupane, Richard Koech, et al.
Biosensors and Bioelectronics X (2023) Vol. 15, pp. 100407-100407
Open Access | Times Cited: 14
A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines
Joffrey Mejias, Djampa Kozlowski, Jackson Goshon, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Unveiling the infectious morphological behaviour of banana crop pathogenic nematodes inhabited from soil medium to pseudostem using an artificial intelligence approach
S.S. Jayakrishna, S. Sankar Ganesh
Computers and Electronics in Agriculture (2025) Vol. 234, pp. 110277-110277
Closed Access
Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis
Liang Wang, Ying Cheng, Islam Md Meftaul, et al.
Analytical Chemistry (2024) Vol. 96, Iss. 20, pp. 8109-8123
Closed Access | Times Cited: 3
Accuracy of an AI-based automated plate reading mobile application for the identification of clinical mastitis-causing pathogens in chromogenic culture media
Breno Luís Nery Garcia, Cristian Marlon de Magalhães Rodrig Martins, Lucas Faria Porto, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Nemacounter: A user-friendly software to accurately phenotype SCN cysts
Joffrey Mejias, Djampa Kozlowski, Jackson Goshon, et al.
(2024)
Closed Access | Times Cited: 2
A reliable and low-cost deep learning model integrating convolutional neural network and transformer structure for fine-grained classification of chicken Eimeria species
Pengguang He, Zhonghao Chen, Yefan He, et al.
Poultry Science (2022) Vol. 102, Iss. 3, pp. 102459-102459
Open Access | Times Cited: 11
The Smart Soil Organism Detector: An instrument and machine learning pipeline for soil species identification
Camila C. Filgueiras, Young-Woon Kim, Kyle Wickings, et al.
Biosensors and Bioelectronics (2022) Vol. 221, pp. 114417-114417
Closed Access | Times Cited: 9
Faster region-based convolutional neural network for plant-parasitic and non-parasitic nematode detection
Natalia Angeline, Nabila Husna Shabrina, Siwi Indarti
Indonesian Journal of Electrical Engineering and Computer Science (2023) Vol. 30, Iss. 1, pp. 316-316
Open Access | Times Cited: 4
Plant Parasitic Nematode Identification in Complex Samples with Deep Learning
Sahil Agarwal, Zachary Charles Curran, Guohao Yu, et al.
Journal of Nematology (2023) Vol. 55, Iss. 1
Open Access | Times Cited: 4
Automatic Identification of Tomato Pests Using Parallel Deep Learning Models
Meiling Huang, Y. C. Chen
Sensors and Materials (2024) Vol. 36, Iss. 5, pp. 1983-1983
Open Access | Times Cited: 1
On the application of Image augmentation for Plant Disease Detection: A Systematic Literature Review
Kwame Antwi, Kwabena Ebo Bennin, Derek Kwaku Pobi Asiedu, et al.
Smart Agricultural Technology (2024) Vol. 9, pp. 100590-100590
Open Access | Times Cited: 1
Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops
Top Bahadur Pun, Roniya Thapa Magar, Richard Koech, et al.
Plants (2024) Vol. 13, Iss. 21, pp. 3041-3041
Open Access | Times Cited: 1
Development of a Deep Learning‐Assisted Mobile Application for the Identification of Nematodes Through Microscopic Images
Naseeb Singh, Ashish Kumar Singh, L.K. Dhruw, et al.
Modern Agriculture (2024) Vol. 2, Iss. 2
Open Access | Times Cited: 1
Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification
Şifa Özsarı, Eda Kumru, Fatih Ekinci, et al.
Sensors (2024) Vol. 24, Iss. 22, pp. 7189-7189
Open Access | Times Cited: 1
Microscopic hyperspectral imaging and an improved detection model based detection of Mycogone perniciosa chlamydospore in soil
Xuan Wei, Yongjie Liu, Qiming Song, et al.
European Journal of Agronomy (2023) Vol. 152, pp. 127007-127007
Closed Access | Times Cited: 2
A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management
Top Bahadur Pun, Arjun Neupane, Richard Koech
Journal of Imaging (2023) Vol. 9, Iss. 11, pp. 240-240
Open Access | Times Cited: 2
Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach
Himer Ávila-George, Miguel De‐la‐Torre, Jorge Sánchez-Garcés, et al.
PeerJ (2023) Vol. 11, pp. e14808-e14808
Open Access | Times Cited: 1
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