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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:

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Srdjan Sladojević, Marko Arsenović, Andraš Anderla, et al.
Computational Intelligence and Neuroscience (2016) Vol. 2016, pp. 1-11
Open Access | Times Cited: 1546

Showing 1-25 of 1546 citing articles:

Deep learning in agriculture: A survey
Andreas Kamilaris, Francesc X. Prenafeta‐Boldú
Computers and Electronics in Agriculture (2018) Vol. 147, pp. 70-90
Open Access | Times Cited: 3107
Deep learning models for plant disease detection and diagnosis
Konstantinos P. Ferentinos
Computers and Electronics in Agriculture (2018) Vol. 145, pp. 311-318
Closed Access | Times Cited: 2188
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mehdi Mohammadi, Ala Al‐Fuqaha, Sameh Sorour, et al.
IEEE Communications Surveys & Tutorials (2018) Vol. 20, Iss. 4, pp. 2923-2960
Open Access | Times Cited: 1257
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
Alvaro Fuentes, Sook Yoon, Sang Ryong Kim, et al.
Sensors (2017) Vol. 17, Iss. 9, pp. 2022-2022
Open Access | Times Cited: 1236
A comparative study of fine-tuning deep learning models for plant disease identification
Edna C. Too, Yujian Li, Sam Njuki, et al.
Computers and Electronics in Agriculture (2018) Vol. 161, pp. 272-279
Closed Access | Times Cited: 932
Applications of Remote Sensing in Precision Agriculture: A Review
Rajendra P. Sishodia, Ram L. Ray, Sudhir Kumar Singh
Remote Sensing (2020) Vol. 12, Iss. 19, pp. 3136-3136
Open Access | Times Cited: 800
Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
Bin Liu, Yun Zhang, Dongjian He, et al.
Symmetry (2017) Vol. 10, Iss. 1, pp. 11-11
Open Access | Times Cited: 654
Plant leaf disease classification using EfficientNet deep learning model
Ümit Atila, Murat Uçar, Kemal Akyol, et al.
Ecological Informatics (2020) Vol. 61, pp. 101182-101182
Closed Access | Times Cited: 654
Identification of plant leaf diseases using a nine-layer deep convolutional neural network
G. Geetharamani, J. Arun Pandian
Computers & Electrical Engineering (2019) Vol. 76, pp. 323-338
Closed Access | Times Cited: 649
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
John Ball, Derek T. Anderson, Chee Seng Chan
Journal of Applied Remote Sensing (2017) Vol. 11, Iss. 04, pp. 1-1
Open Access | Times Cited: 639
Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning
Guan Wang, Yu Sun, Jianxin Wang
Computational Intelligence and Neuroscience (2017) Vol. 2017, pp. 1-8
Open Access | Times Cited: 627
Plant Disease Detection and Classification by Deep Learning
Muhammad Hammad Saleem, Johan Potgieter, Khalid Mahmood Arif
Plants (2019) Vol. 8, Iss. 11, pp. 468-468
Open Access | Times Cited: 614
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
Asheesh K. Singh, Baskar Ganapathysubramanian, Soumik Sarkar, et al.
Trends in Plant Science (2018) Vol. 23, Iss. 10, pp. 883-898
Open Access | Times Cited: 516
How deep learning extracts and learns leaf features for plant classification
Sue Han Lee, Chee Seng Chan, Simon Joseph Mayo, et al.
Pattern Recognition (2017) Vol. 71, pp. 1-13
Open Access | Times Cited: 511
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress
Amy Lowe, Nicola Harrison, Andrew P. French
Plant Methods (2017) Vol. 13, Iss. 1
Open Access | Times Cited: 491
An explainable deep machine vision framework for plant stress phenotyping
Sambuddha Ghosal, David Blystone, Asheesh K. Singh, et al.
Proceedings of the National Academy of Sciences (2018) Vol. 115, Iss. 18, pp. 4613-4618
Open Access | Times Cited: 468
Attention embedded residual CNN for disease detection in tomato leaves
R. Karthik, M. Hariharan, Sundar Anand, et al.
Applied Soft Computing (2019) Vol. 86, pp. 105933-105933
Closed Access | Times Cited: 444
Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
Tawseef Ayoub Shaikh, Tabasum Rasool, Faisal Rasheed Lone
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107119-107119
Closed Access | Times Cited: 430
Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
Artzai Picón, Aitor Álvarez-Gila, Maximiliam Seitz, et al.
Computers and Electronics in Agriculture (2018) Vol. 161, pp. 280-290
Closed Access | Times Cited: 416
Identification and recognition of rice diseases and pests using convolutional neural networks
Chowdhury Rafeed Rahman, Preetom S. Arko, Mohammed Eunus Ali, et al.
Biosystems Engineering (2020) Vol. 194, pp. 112-120
Open Access | Times Cited: 414
A review of the use of convolutional neural networks in agriculture
Andreas Kamilaris, Francesc X. Prenafeta‐Boldú
The Journal of Agricultural Science (2018) Vol. 156, Iss. 3, pp. 312-322
Open Access | Times Cited: 396
An automated detection and classification of citrus plant diseases using image processing techniques: A review
Zahid Iqbal, Muhammad Attique Khan, Muhammad Sharif, et al.
Computers and Electronics in Agriculture (2018) Vol. 153, pp. 12-32
Closed Access | Times Cited: 391
Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network
Jun Liu, Xuewei Wang
Frontiers in Plant Science (2020) Vol. 11
Open Access | Times Cited: 391
Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection
Marko Arsenović, Mirjana Karanovic, Srdjan Sladojević, et al.
Symmetry (2019) Vol. 11, Iss. 7, pp. 939-939
Open Access | Times Cited: 387
Agricultural remote sensing big data: Management and applications
Yanbo Huang, Zhongxin Chen, Tao Yu, et al.
Journal of Integrative Agriculture (2018) Vol. 17, Iss. 9, pp. 1915-1931
Open Access | Times Cited: 378
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