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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:

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: 1241

Showing 1-25 of 1241 citing articles:

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: 2204
Deep Learning for Anomaly Detection: A Survey
Raghavendra Chalapathy, Sanjay Chawla
arXiv (Cornell University) (2019)
Open Access | Times Cited: 1179
Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks
Peng Jiang, Yuehan Chen, Bin Liu, et al.
IEEE Access (2019) Vol. 7, pp. 59069-59080
Open Access | Times Cited: 691
Plant diseases and pests detection based on deep learning: a review
Jun Liu, Xuewei Wang
Plant Methods (2021) Vol. 17, Iss. 1
Open Access | Times Cited: 661
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: 659
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: 652
Translating High-Throughput Phenotyping into Genetic Gain
J. L. Araus, Shawn C. Kefauver, Mainassara Zaman‐Allah, et al.
Trends in Plant Science (2018) Vol. 23, Iss. 5, pp. 451-466
Open Access | Times Cited: 639
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: 619
Plant disease identification from individual lesions and spots using deep learning
Jayme Garcia Arnal Barbedo
Biosystems Engineering (2019) Vol. 180, pp. 96-107
Closed Access | Times Cited: 563
Plant Disease Detection and Classification by Deep Learning—A Review
Lili Li, Shujuan Zhang, Bin Wang
IEEE Access (2021) Vol. 9, pp. 56683-56698
Open Access | Times Cited: 559
Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification
Jayme Garcia Arnal Barbedo
Computers and Electronics in Agriculture (2018) Vol. 153, pp. 46-53
Closed Access | Times Cited: 536
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: 521
Factors influencing the use of deep learning for plant disease recognition
Jayme Garcia Arnal Barbedo
Biosystems Engineering (2018) Vol. 172, pp. 84-91
Closed Access | Times Cited: 470
Tomato plant disease detection using transfer learning with C-GAN synthetic images
Amreen Abbas, Sweta Jain, Mahesh Gour, et al.
Computers and Electronics in Agriculture (2021) Vol. 187, pp. 106279-106279
Closed Access | Times Cited: 449
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: 446
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: 437
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: 420
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: 395
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: 392
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: 389
CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture
Yangyang Zheng, Jianlei Kong, Xuebo Jin, et al.
Sensors (2019) Vol. 19, Iss. 5, pp. 1058-1058
Open Access | Times Cited: 368
Recent advances in image processing techniques for automated leaf pest and disease recognition – A review
Lawrence C. Ngugi, Moataz Abelwahab, Mohammed Abo‐Zahhad
Information Processing in Agriculture (2020) Vol. 8, Iss. 1, pp. 27-51
Open Access | Times Cited: 365
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach
Sk Mahmudul Hassan, Arnab Kumar Maji, Michał Jasiński, et al.
Electronics (2021) Vol. 10, Iss. 12, pp. 1388-1388
Open Access | Times Cited: 344
Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification
Jinzhu Lu, Lijuan Tan, Huanyu Jiang
Agriculture (2021) Vol. 11, Iss. 8, pp. 707-707
Open Access | Times Cited: 330
AI-powered banana diseases and pest detection
Michael Gomez Selvaraj, Alejandro Perdomo Vergara, Henry Ruiz, et al.
Plant Methods (2019) Vol. 15, Iss. 1
Open Access | Times Cited: 329
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