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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:

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth
Seungtaek Jeong, Jonghan Ko, Taehwan Shin, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning
Md Didarul Islam, Liping Di, Faisal Mueen Qamer, et al.
Remote Sensing (2023) Vol. 15, Iss. 9, pp. 2374-2374
Open Access | Times Cited: 29
Construction and Optimization of Integrated Yield Prediction Model Based on Phenotypic Characteristics of Rice Grown in Small–Scale Plantations
Jihong Sun, Peng Tian, Zhaowen Li, et al.
Agriculture (2025) Vol. 15, Iss. 2, pp. 181-181
Open Access | Times Cited: 1
Mechanisms and modelling approaches for excessive rainfall stress on cereals: Waterlogging, submergence, lodging, pests and diseases
Yean‐Uk Kim, Heidi Webber, S.G.K. Adiku, et al.
Agricultural and Forest Meteorology (2023) Vol. 344, pp. 109819-109819
Open Access | Times Cited: 21
Corn yield prediction in site-specific management zones using proximal soil sensing, remote sensing, and machine learning approach
Bere Benjamin Bantchına, Muhammad Qaswar, Selçuk Arslan, et al.
Computers and Electronics in Agriculture (2024) Vol. 225, pp. 109329-109329
Closed Access | Times Cited: 6
Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction
Seungtaek Jeong, Jonghan Ko, Jong-Oh Ban, et al.
Ecological Informatics (2024) Vol. 84, pp. 102886-102886
Open Access | Times Cited: 5
P3S: Pertinent Privacy-Preserving Scheme for Remotely Sensed Environmental Data in Smart Cities
Fahad Algarni, Mohammad Ayoub Khan, Wedad Alawad, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 5905-5918
Open Access | Times Cited: 11
Machine learning constructs color features to accelerate development of long-term continuous water quality monitoring
Mengyuan Zhang, Yanquan Huang, Dongsheng Xie, et al.
Journal of Hazardous Materials (2023) Vol. 461, pp. 132612-132612
Closed Access | Times Cited: 11
Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review
Luís Silva, Luís Alcino Conceição, Fernando Cebola Lidon, et al.
Agriculture (2023) Vol. 13, Iss. 4, pp. 835-835
Open Access | Times Cited: 10
Remote Sensing Data for Predicting Crop Growth
Yunan Li, Sahraoui Dhelim, Liming Chen, et al.
Lecture notes in computer science (2025), pp. 12-26
Closed Access
Comprehensive analysis of methods for estimating actual paddy evapotranspiration—A review
Kiran Bala Behura, S. K. Raul, Jagadish Chandra Paul, et al.
Frontiers in Water (2025) Vol. 7
Open Access
Vorhersage von landwirtschaftlichen Erträgen und Wachstum
Alexander Münzberg, Miro Miranda Lorenz
Springer eBooks (2025), pp. 153-167
Closed Access
Integrating machine learning with agroecosystem modelling: Current state and future challenges
Meshach Ojo Aderele, A K Srivastava, Klaus Butterbach‐Bahl, et al.
European Journal of Agronomy (2025) Vol. 168, pp. 127610-127610
Open Access
Cell Wall–Based Machine Learning Models to Predict Plant Growth Using Onion Epidermis
Celia Khoulali, Juan Manuel Pastor, Javier Galeano, et al.
International Journal of Molecular Sciences (2025) Vol. 26, Iss. 7, pp. 2946-2946
Open Access
Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning
Lily‐belle Sweet, Ioannis N. Athanasiadis, Ron van Bree, et al.
One Earth (2025) Vol. 8, Iss. 4, pp. 101233-101233
Open Access
Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks
Fan Yang, Yuhuan Zhou, Jiayi Du, et al.
Plant Methods (2024) Vol. 20, Iss. 1
Open Access | Times Cited: 3
Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation
Jonghan Ko, Taehwan Shin, Jiwoo Kang, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 3
An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges
Nidhi Parashar, Prashant Johri, Arfat Ahmad Khan, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2024) Vol. 80, Iss. 1, pp. 389-425
Open Access | Times Cited: 3
Modeling Climate Change Scenarios for Spring Barley in Southeast of Almaty in Kazakhstan Using the LINTUL Approach
Aidana Sabitova, Gulnur Suleımanova, Тефиде Кизилдениз, et al.
Black Sea Journal of Engineering and Science (2024) Vol. 7, Iss. 3, pp. 465-472
Open Access | Times Cited: 1
Predicting Winter Wheat Emergence and Stem Elongation Time using CNN
Yunan Li, Sahraoui Dhelim, Tahar Kechadi
(2024) Vol. 42, pp. 153-160
Open Access | Times Cited: 1
Data-driven optimization of nose profiles for water entry impact load reduction
Kai Lv, Enfan Lin, Yong-Ze Liu, et al.
Ocean Engineering (2024) Vol. 315, pp. 119851-119851
Closed Access | Times Cited: 1
Deep Learning: A Catalyst for Sustainable Agriculture Transformation
Shabnam Choudhury, Biplab Banerjee
Studies in big data (2024), pp. 173-199
Closed Access | Times Cited: 1
Constructing a Model of Poplus spp. Growth Rate Based on the Model Fusion and Analysis of Its Growth Rate Differences and Distribution Characteristics under Different Classes of Environmental Indicators
Biao Zhang, Guowei Liu, Zhongke Feng, et al.
Forests (2023) Vol. 14, Iss. 10, pp. 2073-2073
Open Access | Times Cited: 3
Machine Learning Application in G.I.S. and Remote Sensing: An Overview
Anjeel Upreti
(2022)
Open Access | Times Cited: 4
A Comprehensive Review on Deep learning Techniques for Crop Yield Prediction
Priti Prakash Jorvekar, Sharmila K. Wagh, Jayashree R. Prasad
(2023)
Closed Access | Times Cited: 2
Machine learning application in GIS and remote sensing: An overview
Anjeel Upreti
International Journal of Multidisciplinary Research and Growth Evaluation (2022), pp. 546-553
Open Access | Times Cited: 3
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