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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 learning: as the new frontier in high-throughput plant phenotyping
Sunny Arya, Karansher Singh Sandhu, Jagmohan Singh, et al.
Euphytica (2022) Vol. 218, Iss. 4
Closed Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses
Rajib Roychowdhury, Soumya Prakash Das, Amber Gupta, et al.
Genes (2023) Vol. 14, Iss. 6, pp. 1281-1281
Open Access | Times Cited: 77
Important wheat diseases in the US and their management in the 21st century
Jagdeep Singh, Bhavit Chhabra, Ali Raza, et al.
Frontiers in Plant Science (2023) Vol. 13
Open Access | Times Cited: 44
Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model
Na Ma, Yaxin Su, Lexin Yang, et al.
Sensors (2024) Vol. 24, Iss. 5, pp. 1654-1654
Open Access | Times Cited: 22
Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives
Haiyu Tao, Shan Xu, Yongchao Tian, et al.
Plant Communications (2022) Vol. 3, Iss. 6, pp. 100344-100344
Open Access | Times Cited: 59
Integrating blockchain and deep learning for intelligent greenhouse control and traceability
Tarek Frikha, Jalel Ktari, Bechir Zalila, et al.
Alexandria Engineering Journal (2023) Vol. 79, pp. 259-273
Open Access | Times Cited: 25
An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture
Danuta Cembrowska-Lech, Adrianna Krzemińska, Tymoteusz Miller, et al.
Biology (2023) Vol. 12, Iss. 10, pp. 1298-1298
Open Access | Times Cited: 25
Cyber-agricultural systems for crop breeding and sustainable production
Soumik Sarkar, Baskar Ganapathysubramanian, Arti Singh, et al.
Trends in Plant Science (2023) Vol. 29, Iss. 2, pp. 130-149
Open Access | Times Cited: 24
Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses
Ting‐Chi Wen, Jianhong Li, Qi Wang, et al.
The Science of The Total Environment (2023) Vol. 899, pp. 165626-165626
Closed Access | Times Cited: 21
Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms
Takashi Tanaka, Sheng Wang, Johannes Ravn Jørgensen, et al.
Drones (2024) Vol. 8, Iss. 6, pp. 212-212
Open Access | Times Cited: 11
Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet
He Li, Hongtao Shi, Anghong Du, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 34
Multi-Omics Techniques for Soybean Molecular Breeding
Pan Cao, Ying Zhao, Fengjiao Wu, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 9, pp. 4994-4994
Open Access | Times Cited: 32
Adapting the Segment Anything Model for Plant Recognition and Automated Phenotypic Parameter Measurement
Wenqi Zhang, L. Minh Dang, Le Quan Nguyen, et al.
Horticulturae (2024) Vol. 10, Iss. 4, pp. 398-398
Open Access | Times Cited: 7
Review of deep learning-based methods for non-destructive evaluation of agricultural products
Zhenye Li, Dongyi Wang, Tingting Zhu, et al.
Biosystems Engineering (2024) Vol. 245, pp. 56-83
Closed Access | Times Cited: 6
The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review
Rui-Feng Wang, Wen‐Hao Su
Agriculture (2024) Vol. 14, Iss. 8, pp. 1225-1225
Open Access | Times Cited: 6
Explainable deep learning in plant phenotyping
Sakib Mostafa, Debajyoti Mondal, Karim Panjvani, et al.
Frontiers in Artificial Intelligence (2023) Vol. 6
Open Access | Times Cited: 15
Computer Vision and Deep Learning as Tools for Leveraging Dynamic Phenological Classification in Vegetable Crops
Leandro Rodrigues, Sandro Augusto Magalhães, Daniel Queirós da Silva, et al.
Agronomy (2023) Vol. 13, Iss. 2, pp. 463-463
Open Access | Times Cited: 14
ClGanNet: A novel method for maize leaf disease identification using ClGan and deep CNN
Vivek Sharma, Ashish Kumar Tripathi, Purva Daga, et al.
Signal Processing Image Communication (2023) Vol. 120, pp. 117074-117074
Closed Access | Times Cited: 14
A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping
Dapeng Ye, Libin Wu, Xiaobin Li, et al.
Plants (2023) Vol. 12, Iss. 8, pp. 1698-1698
Open Access | Times Cited: 13
A High‐Throughput Phenotyping Pipeline for Quinoa (Chenopodium quinoa) Panicles Using Image Analysis With Convolutional Neural Networks
Flavio Lozano‐Isla, Lydia Kienbaum, Bettina I. G. Haussmann, et al.
Plant Breeding (2025)
Open Access
Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change
Hoa Thi Nguyen, Md. Arifur Rahman Khan, Thuong Thi Nguyen, et al.
Plants (2025) Vol. 14, Iss. 6, pp. 907-907
Open Access
SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques
Arpan Kumar Maji, Sudeep Marwaha, Sudhir Kumar, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 19
Predictor bias in genomic and phenomic selection
Hermann Gregor Dallinger, Franziska Löschenberger, Herbert Bistrich, et al.
Theoretical and Applied Genetics (2023) Vol. 136, Iss. 11
Open Access | Times Cited: 12
Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud
Ajay Kumar Patel, Eun-Sung Park, Hongseok Lee, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 8492-8507
Open Access | Times Cited: 11
HUMRC-PS: Revolutionizing plant phenotyping through Regional Convolutional Neural Networks and Pelican Search Optimization
P. Rajesh Kumar, A. Senthilselvi, I. Manju, et al.
Evolving Systems (2024) Vol. 15, Iss. 6, pp. 2211-2230
Closed Access | Times Cited: 3
M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers
J. Dhakshayani, B. Surendiran
Agriculture (2023) Vol. 13, Iss. 6, pp. 1238-1238
Open Access | Times Cited: 7
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