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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:

High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
Peng Song, Jinglu Wang, Xinyu Guo, et al.
The Crop Journal (2021) Vol. 9, Iss. 3, pp. 633-645
Open Access | Times Cited: 165

Showing 1-25 of 165 citing articles:

Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis
Julietta Moustaka, Michael Moustakas
Biosensors (2023) Vol. 13, Iss. 8, pp. 796-796
Open Access | Times Cited: 53
Opportunity and challenges of phenotyping plant salt tolerance
Yuncai Hu, Urs Schmidhalter
Trends in Plant Science (2023) Vol. 28, Iss. 5, pp. 552-566
Closed Access | Times Cited: 39
The next era of crop domestication starts now
Aubrey Streit Krug, Emily B. M. Drummond, David L. Van Tassel, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 14
Open Access | Times Cited: 39
Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture
Sakshi Balyan, Harsita Jangir, Shakti Nath Tripathi, et al.
Sustainability (2024) Vol. 16, Iss. 2, pp. 475-475
Open Access | Times Cited: 31
Temperature‐smart plants: A new horizon with omics‐driven plant breeding
Ali Raza, Shanza Bashir, Tushar Khare, et al.
Physiologia Plantarum (2024) Vol. 176, Iss. 1
Open Access | Times Cited: 20
3D reconstruction and characterization of cotton bolls in situ based on UAV technology
Shunfu Xiao, Shuaipeng Fei, Yulu Ye, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 209, pp. 101-116
Closed Access | Times Cited: 14
Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why
Salvador Castillo‐Gironés, Sandra Munera, Marcelino Martínez‐Sober, et al.
Computers and Electronics in Agriculture (2025) Vol. 230, pp. 109938-109938
Open Access | Times Cited: 1
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
Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding
Muhammad Hafeez Ullah Khan, Shoudong Wang, Jun Wang, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 19, pp. 11156-11156
Open Access | Times Cited: 59
Pathways to engineering the phyllosphere microbiome for sustainable crop production
Chengfang Zhan, Haruna Matsumoto, Yufei Liu, et al.
Nature Food (2022) Vol. 3, Iss. 12, pp. 997-1004
Closed Access | Times Cited: 56
Early Drought Stress Warning in Plants: Color Pictures of Photosystem II Photochemistry
Michael Moustakas, Ilektra Sperdouli, Julietta Moustaka
Climate (2022) Vol. 10, Iss. 11, pp. 179-179
Open Access | Times Cited: 46
Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies
Lei Li, Muhammad Adeel Hassan, Shurong Yang, et al.
The Crop Journal (2022) Vol. 10, Iss. 5, pp. 1303-1311
Open Access | Times Cited: 39
Utilization of Spectral Indices for High-Throughput Phenotyping
Rupesh Tayade, Jung-Beom Yoon, Liny Lay, et al.
Plants (2022) Vol. 11, Iss. 13, pp. 1712-1712
Open Access | Times Cited: 37
Maize and heat stress: Physiological, genetic, and molecular insights
Ivica Djalović, Sayanta Kundu, Rajeev N. Bahuguna, et al.
The Plant Genome (2023)
Open Access | Times Cited: 29
AIseed: An automated image analysis software for high-throughput phenotyping and quality non-destructive testing of individual plant seeds
Keling Tu, Weifeng Wu, Ying Cheng, et al.
Computers and Electronics in Agriculture (2023) Vol. 207, pp. 107740-107740
Closed Access | Times Cited: 21
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
Hybrid Rice Production: A Worldwide Review of Floral Traits and Breeding Technology, with Special Emphasis on China
Humera Ashraf, Fozia Ghouri, Faheem Shehzad Baloch, et al.
Plants (2024) Vol. 13, Iss. 5, pp. 578-578
Open Access | Times Cited: 11
Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence—A Critical Review
Lixia Sun, Mingyu Lai, Fozia Ghouri, et al.
Plants (2024) Vol. 13, Iss. 19, pp. 2676-2676
Open Access | Times Cited: 9
Advances in field-based high-throughput photosynthetic phenotyping
Peng Fu, Christopher M. Montes, Matthew H. Siebers, et al.
Journal of Experimental Botany (2022) Vol. 73, Iss. 10, pp. 3157-3172
Open Access | Times Cited: 33
A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey
Antoine Harfouche, Farid Nakhle, Antoine Harfouche, et al.
Trends in Plant Science (2022) Vol. 28, Iss. 2, pp. 154-184
Open Access | Times Cited: 30
A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning
Keling Tu, Shaozhe Wen, Ying Cheng, et al.
Plant Methods (2022) Vol. 18, Iss. 1
Open Access | Times Cited: 29
End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses
Chu Zhang, Lei Zhou, Qinlin Xiao, et al.
Plant Phenomics (2022) Vol. 2022
Open Access | Times Cited: 29
End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images
Sourav Bhadra, Vasit Sagan, Juan Skobalski, et al.
Precision Agriculture (2023) Vol. 25, Iss. 2, pp. 834-864
Open Access | Times Cited: 20
Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform
Yinglun Li, Weiliang Wen, Jiangchuan Fan, et al.
Plant Phenomics (2023) Vol. 5
Open Access | Times Cited: 19
Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms
Ruichao Guo, Jilong Xie, Jiaxi Zhu, et al.
Computers and Electronics in Agriculture (2023) Vol. 211, pp. 108014-108014
Closed Access | Times Cited: 19
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