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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:

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

Showing 15 citing articles:

Machine learning in construction and demolition waste management: Progress, challenges, and future directions
Yu Gao, Jiayuan Wang, Xiaoxiao Xu
Automation in Construction (2024) Vol. 162, pp. 105380-105380
Closed Access | Times Cited: 23
Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning
Shidan Zhu, Ningbo Cui, Li Guo, et al.
Computers and Electronics in Agriculture (2024) Vol. 221, pp. 108943-108943
Closed Access | Times Cited: 7
Intelligent Estimation of Soil Hydraulic Parameters: A Kelm-Pso Integrated Framework for Border Irrigation Optimization
Wen Zhou, Dan Bai, Yibo Li, et al.
(2025)
Closed Access
Utilizing convolutional neural network (CNN) for orchard irrigation decision-making
Atsushi Okayama, Atsushi Yamamoto, Masaomi Kimura, et al.
Environmental Monitoring and Assessment (2025) Vol. 197, Iss. 2
Open Access
Geochemical and isotopic studies of the Douda-Damerjogue aquifer (Republic of Djibouti): Origin of high nitrate and fluoride, spatial distribution, associated health risk assessment and prediction of water quality using machine learning
M.O. Awaleh, Tiziano Boschetti, Christelle Marlin, et al.
The Science of The Total Environment (2025) Vol. 967, pp. 178789-178789
Closed Access
Water and nitrogen supply at spatially distinct locations improves cotton water productivity and nitrogen use efficiency and yield under drip irrigation
Kai Liu, Huan Liao, Haibo Hao, et al.
Agricultural Water Management (2024) Vol. 296, pp. 108808-108808
Open Access | Times Cited: 3
Soil microbiome: An intrinsic driver for climate smart agriculture
Hemant J. Purohit, Prabhakar D. Pandit, Rajesh Pal, et al.
Journal of Agriculture and Food Research (2024), pp. 101433-101433
Open Access | Times Cited: 2
Kiwi 4.0: In Vivo Real-Time Monitoring to Improve Water Use Efficiency in Yellow Flesh Actinidia chinensis
Filippο Vurro, Luigi Manfrini, Alexandra Boini, et al.
Biosensors (2024) Vol. 14, Iss. 5, pp. 226-226
Open Access | Times Cited: 1
Hydrothermal bio-oil yield and higher heating value of high moisture and lipid biomass: Machine Learning modeling and feature response behavior analysis
Xiangjie Liu, Xin Zhang, Khantaphong Charoenkal, et al.
Journal of the Energy Institute (2024), pp. 101859-101859
Closed Access | Times Cited: 1
Leveraging Incremental Decision Trees and In-Vivo Biosensors for an Explainable Plant Health Monitoring System
Giovanni Panella, Pietro Ducange, Manuele Bettelli, et al.
(2024), pp. 1-8
Closed Access
An Explainable Smart Agriculture System based on In- Vivo Biosensors
Riccardo Pecori, Giovanni Panella, Filippο Vurro, et al.
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2024), pp. 1-8
Closed Access
Machine Learning and Thermal Imaging in Precision Agriculture
Kostas-Gkouram Mirzaev, Chairi Kiourt
Lecture notes in networks and systems (2024), pp. 168-187
Closed Access
Basics of AI for Water Management
Manish Kumar Goyal, Sachidanand Kumar, Akhilesh Gupta
SpringerBriefs in water science and technology (2024), pp. 1-16
Closed Access
State-of-the art-on irrigation water quality management using data-driven methods: Practical application, limitations, and prospective directions
Ali El Bilali, Abdeslam Taleb
Physics and Chemistry of the Earth Parts A/B/C (2024) Vol. 136, pp. 103794-103794
Closed Access
Automated Smart Irrigation System for Potato Cultivation Using Machine Learning Techniques: A Case Study
Puja Saha, Teddyson Marbaniang, Lalhmingliana, et al.
Lecture notes in networks and systems (2024), pp. 113-126
Closed Access
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