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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:

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction
Junjun Cao, Huijing Wang, Jinxiao Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 7, pp. 1707-1707
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Hybrid forecasting: blending climate predictions with AI models
Louise Slater, Louise Arnal, Marie‐Amélie Boucher, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 9, pp. 1865-1889
Open Access | Times Cited: 84
Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe
Seyed Babak Haji Seyed Asadollah, Antonio Jódar-Abellán, Miguel Ángel Pardo Picazo
Agricultural Systems (2024) Vol. 218, pp. 103955-103955
Open Access | Times Cited: 16
Impacts of environment and human activity on grid-scale land cropping suitability and optimization of planting structure, measured based on the MaxEnt model
Shiliang Yang, Huimin Wang, Jinping Tong, et al.
The Science of The Total Environment (2022) Vol. 836, pp. 155356-155356
Closed Access | Times Cited: 51
Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models
Louise Slater, Louise Arnal, Marie‐Amélie Boucher, et al.
(2022)
Open Access | Times Cited: 23
Synergetic use of geospatial and machine learning techniques in modelling landslide susceptibility in parts of Shimla to Kinnaur National Highway, Himachal Pradesh
Rahul Deb Das, Shovan Lal Chattoraj, Mohit Singh, et al.
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 3, pp. 4163-4183
Closed Access | Times Cited: 5
Estimation of soybean yield based on high-throughput phenotyping and machine learning
Xiuni Li, Menggen Chen, Shuyuan He, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 5
Seasonal prediction of summer extreme precipitation frequencies over Southwest China based on machine learning
Yanbo Nie, Jianqi Sun, Jiehua Ma
Atmospheric Research (2023) Vol. 294, pp. 106947-106947
Closed Access | Times Cited: 11
Machine Learning and Deep Learning in Remote Sensing Data Analysis
Hankui K. Zhang, Shi Qiu, Ji Won Suh, et al.
Elsevier eBooks (2024)
Closed Access | Times Cited: 4
Crop Yield Prediction in Agriculture: A Comprehensive Review of Machine Learning and Deep Learning Approaches, with Insights for Future Research and Sustainability
Md Abu Jabed, Masrah Azrifah Azmi Murad
Heliyon (2024) Vol. 10, Iss. 24, pp. e40836-e40836
Open Access | Times Cited: 4
BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation
Lei Zhang, Changchun Li, Xifang Wu, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 4
Machine learning methods in soil fertility forecasting
V V Vershinin, Д. А. Шаповалов, Nikita M. Pochyomin, et al.
Zemleustrojstvo, kadastr i monitoring zemel' (Land management, cadastre and land monitoring) (2025), Iss. 1, pp. 42-50
Closed Access
Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting
Juan Carlos Moreno Sánchez, Héctor Mesa, Adrián Trueba Espinosa, et al.
Smart Agricultural Technology (2025), pp. 100791-100791
Open Access
Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought
Yi Luo, Huijing Wang, Junjun Cao, et al.
Precision Agriculture (2024) Vol. 25, Iss. 4, pp. 1982-2006
Closed Access | Times Cited: 3
Ensemble regression based Extra Tree Regressor for hybrid crop yield prediction system
T. Sudhamathi, K. Perumal
Measurement Sensors (2024) Vol. 35, pp. 101277-101277
Open Access | Times Cited: 3
Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning
Yuhao Ang, Helmi Zulhaidi Mohd Shafri, Yang Ping Lee, et al.
Earth Science Informatics (2022) Vol. 15, Iss. 4, pp. 2349-2367
Closed Access | Times Cited: 14
Crop Yield Prediction Using Deep Reinforcement Learning
Sudhakar Deivasigamani, A. Jaya Mabel Rani, R Natchadalingam, et al.
(2023), pp. 137-142
Closed Access | Times Cited: 8
DISEASES OF WILD APPLE SIVERS IN THE DZUNGARIAN AND TRANS-ILI ALATAU
Sagi S. Soltanbekov, Zh.M. Isina, Zh.K. Dzhumanova, et al.
Bulletin of Korkyt Ata Kyzylorda University (2024) Vol. 1, pp. 196-205
Open Access | Times Cited: 2
Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions
Guangpo Geng, Qian Gu, Hongkui Zhou, et al.
Ecological Informatics (2024), pp. 102972-102972
Open Access | Times Cited: 2
A Systematic Review on Crop Yield Prediction Using Machine Learning
Moon Halder, Ayon Datta, K. Siam, et al.
Lecture notes in networks and systems (2023), pp. 658-667
Closed Access | Times Cited: 6
Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF)
Xiaoyan Kang, Changping Huang, Lifu Zhang, et al.
Remote Sensing of Environment (2023) Vol. 299, pp. 113861-113861
Closed Access | Times Cited: 6
On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat
M. Palka, Manschadi A.M.
Precision Agriculture (2024) Vol. 25, Iss. 5, pp. 2394-2420
Open Access | Times Cited: 1
Wheat Crop Yield Prediction Using Machine Learning
Deepak Kumar, Yash Kumar, Akhilesh Gulati, et al.
2021 International Conference on Data Analytics for Business and Industry (ICDABI) (2022), pp. 433-437
Closed Access | Times Cited: 6
Wheat Yield Prediction Using Machine Learning: A Survey
Taye Girma Debelee, Samuel Rahimeto Kebede, Fraol Gelana Waldamichael, et al.
Communications in computer and information science (2023), pp. 114-132
Closed Access | Times Cited: 2
XGBoost sequential system for the prediction of Persian lemon crop yield
Mildred Virginia López-Segura, Alberto A. Aguilar‐Lasserre, Gregorio Fernández-Lambert, et al.
Crop Science (2023) Vol. 65, Iss. 1
Closed Access | Times Cited: 2
MILK COMPOSITION, TECHNOLOGICAL PROPERTIES AND PRODUCTION OF DAIRY PRODUCTS OF GOATS OF THE DAIRY DIRECTION
Alma Temirzhanova, Abeldinov Rustem, B. Ateikhan, et al.
Bulletin of Korkyt Ata Kyzylorda University (2024) Vol. 1, pp. 177-185
Open Access
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