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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:

Water status estimation of cherry trees using infrared thermal imagery coupled with supervised machine learning modeling
Marcos Carrasco-Benavides, Claudia Gonzalez Viejo, Eden Tongson, et al.
Computers and Electronics in Agriculture (2022) Vol. 200, pp. 107256-107256
Closed Access | Times Cited: 10

Showing 10 citing articles:

Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data
Ruiqi Du, Youzhen Xiang, Fucang Zhang, et al.
Agricultural Water Management (2024) Vol. 298, pp. 108856-108856
Open Access | Times Cited: 7
How can agricultural water production be promoted？ a review on machine learning for irrigation
Hairong Gao, Lili Zhangzhong, Wengang Zheng, et al.
Journal of Cleaner Production (2023) Vol. 414, pp. 137687-137687
Closed Access | Times Cited: 15
Current trends in the use of thermal imagery in assessing plant stresses: A review
Reza Adhitama Putra Hernanda, Hoonsoo Lee, J K Cho, et al.
Computers and Electronics in Agriculture (2024) Vol. 224, pp. 109227-109227
Closed Access | Times Cited: 5
Improving maize water stress diagnosis accuracy by integrating multimodal UAVs data and leaf area index inversion model
Qi Liu, Xiaolong Hu, Yiqiang Zhang, et al.
Agricultural Water Management (2025) Vol. 312, pp. 109407-109407
Open Access
A practical data-driven approach for precise stem water potential monitoring in pistachio and almond orchards using supervised machine learning algorithms
Mehrad Mortazavi, Stefano Carpin, Arash Toudeshki, et al.
Computers and Electronics in Agriculture (2025) Vol. 231, pp. 110004-110004
Open Access
Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review
Soo Been Cho, Hidayat Mohamad Soleh, Ji Won Choi, et al.
Sensors (2024) Vol. 24, Iss. 19, pp. 6313-6313
Open Access | Times Cited: 2
New developments and opportunities for AI in viticulture, pomology, and soft-fruit research: a mini-review and invitation to contribute articles
Sigfredo Fuentes, Eden Tongson, Claudia Gonzalez Viejo
Frontiers in Horticulture (2023) Vol. 2
Open Access | Times Cited: 4
Predicting Tree Water Status in Pistachio and Almond Orchards Using Supervised Machine Learning
Mehrad Mortazavi, Reza Ehsani, Stefano Carpin, et al.
(2023)
Closed Access | Times Cited: 2
Machine Learning for Global Food Security: A Concise Overview
Nusrat Jahan, Thangarajah Akilan, Aparna Phalke
(2022) Vol. abs/2006.11391, pp. 63-68
Closed Access | Times Cited: 3
A Cotton Leaf Water Potential Prediction Model Based on Particle Swarm Optimisation of the LS-SVM Model
Y. Gao, Tiebiao Zhao, Zhong Zheng, et al.
Agronomy (2023) Vol. 13, Iss. 12, pp. 2929-2929
Open Access | Times Cited: 1
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