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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 Field-Phenotyping Tools for Plant Breeding and Precision Agriculture
Aakash Chawade, Joost van Ham, Hanna Blomquist, et al.
Agronomy (2019) Vol. 9, Iss. 5, pp. 258-258
Open Access | Times Cited: 204

Showing 1-25 of 204 citing articles:

Applications of hyperspectral imaging in plant phenotyping
Rijad Sarić, Viet Duc Nguyen, Timothy Burge, et al.
Trends in Plant Science (2022) Vol. 27, Iss. 3, pp. 301-315
Closed Access | Times Cited: 147
Sustainable Crop and Weed Management in the Era of the EU Green Deal: A Survival Guide
Alexandros Tataridas, Panagiotis Kanatas, Antonia Chatzigeorgiou, et al.
Agronomy (2022) Vol. 12, Iss. 3, pp. 589-589
Open Access | Times Cited: 129
Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning
Yinglun Li, Weiliang Wen, Miao Teng, et al.
Computers and Electronics in Agriculture (2022) Vol. 193, pp. 106702-106702
Closed Access | Times Cited: 86
High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion
Huichun Zhang, Yufeng Ge, Xinyan Xie, et al.
Plant Methods (2022) Vol. 18, Iss. 1
Open Access | Times Cited: 83
Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs
Mohsen Yoosefzadeh-Najafabadi, Mohsen Hesami, Milad Eskandari
Genes (2023) Vol. 14, Iss. 4, pp. 777-777
Open Access | Times Cited: 50
A concept for application of integrated digital technologies to enhance future smart agricultural systems
Girma Gebresenbet, Techane Bosona, David J. Patterson, et al.
Smart Agricultural Technology (2023) Vol. 5, pp. 100255-100255
Open Access | Times Cited: 49
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: 35
The role of food industries in sustainability transition: a review
S. R. Mahadeva Prasanna, Praveen Verma, Suman Bodh
Environment Development and Sustainability (2024)
Closed Access | Times Cited: 18
Physiological Responses of Selected Vegetable Crop Species to Water Stress
Eszter Nemeskéri, L. Helyes
Agronomy (2019) Vol. 9, Iss. 8, pp. 447-447
Open Access | Times Cited: 118
High-resolution satellite imagery applications in crop phenotyping: An overview
Chongyuan Zhang, Afef Marzougui, Sindhuja Sankaran
Computers and Electronics in Agriculture (2020) Vol. 175, pp. 105584-105584
Open Access | Times Cited: 115
Species-independent analytical tools for next-generation agriculture
Tedrick Thomas Salim Lew, Rajani Sarojam, In‐Cheol Jang, et al.
Nature Plants (2020) Vol. 6, Iss. 12, pp. 1408-1417
Open Access | Times Cited: 109
Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives
Vadim G. Lebedev, Т. Н. Лебедева, Aleksey Chernodubov, et al.
Forests (2020) Vol. 11, Iss. 11, pp. 1190-1190
Open Access | Times Cited: 107
Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing
Huajian Liu, Brooke Bruning, Trevor Garnett, et al.
Computers and Electronics in Agriculture (2020) Vol. 175, pp. 105621-105621
Closed Access | Times Cited: 102
Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives
Abbas Atefi, Yufeng Ge, Santosh Pitla, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 93
Remote Sensing of Diseases
Erich-Christian Oerke
Annual Review of Phytopathology (2020) Vol. 58, Iss. 1, pp. 225-252
Open Access | Times Cited: 82
Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming
Caiwang Zheng, Amr Abd‐Elrahman, Vance M. Whitaker
Remote Sensing (2021) Vol. 13, Iss. 3, pp. 531-531
Open Access | Times Cited: 81
Advances in optical phenotyping of cereal crops
Dawei Sun, Kelly R. Robbins, Nicolás Morales, et al.
Trends in Plant Science (2021) Vol. 27, Iss. 2, pp. 191-208
Closed Access | Times Cited: 81
Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning
Junfeng Gao, Jesper Cairo Westergaard, Ea Høegh Riis Sundmark, et al.
Knowledge-Based Systems (2021) Vol. 214, pp. 106723-106723
Open Access | Times Cited: 77
Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions
Mohammed A. Naser, Raj Khosla, Louis Longchamps, et al.
Remote Sensing (2020) Vol. 12, Iss. 5, pp. 824-824
Open Access | Times Cited: 72
Review: Application of Artificial Intelligence in Phenomics
Shona Nabwire, Hyun Kwon Suh, Moon S. Kim, et al.
Sensors (2021) Vol. 21, Iss. 13, pp. 4363-4363
Open Access | Times Cited: 59
Integrating artificial intelligence and high-throughput phenotyping for crop improvement
Mansoor Sheikh, Farooq Iqra, Hamadani Ambreen, et al.
Journal of Integrative Agriculture (2023) Vol. 23, Iss. 6, pp. 1787-1802
Open Access | Times Cited: 39
Image-Based High-Throughput Phenotyping in Horticultural Crops
Alebel Mekuriaw Abebe, Younguk Kim, Jae-Young Kim, et al.
Plants (2023) Vol. 12, Iss. 10, pp. 2061-2061
Open Access | Times Cited: 35
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: 12
An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower
Luana Centorame, Thomas Gasperini, Alessio Ilari, et al.
Agronomy (2024) Vol. 14, Iss. 4, pp. 719-719
Open Access | Times Cited: 9
A systematic review on precision agriculture applied to sunflowers, the role of hyperspectral imaging
Luana Centorame, Alessio Ilari, Andrea Gatto, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109097-109097
Open Access | Times Cited: 9
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