Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea
Seungtaek Jeong, Jonghan Ko, Jong‐Min Yeom
The Science of The Total Environment (2021) Vol. 802, pp. 149726-149726
Open Access | Times Cited: 97

Showing 1-25 of 97 citing articles:

Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
Tawseef Ayoub Shaikh, Tabasum Rasool, Faisal Rasheed Lone
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107119-107119
Closed Access | Times Cited: 451
A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing
Priyanga Muruganantham, Santoso Wibowo, Srimannarayana Grandhi, et al.
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 1990-1990
Open Access | Times Cited: 180
A comparative study of deep learning and Internet of Things for precision agriculture
T. Saranya, C. Deisy, S. Sridevi, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106034-106034
Closed Access | Times Cited: 127
Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review
Abhasha Joshi, Biswajeet Pradhan, Shilpa Gite, et al.
Remote Sensing (2023) Vol. 15, Iss. 8, pp. 2014-2014
Open Access | Times Cited: 82
Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk
Tawseef Ayoub Shaikh, Waseem Ahmad Mir, Tabasum Rasool, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 7, pp. 4557-4597
Closed Access | Times Cited: 78
Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation
Li Luo, Shikun Sun, Jing Xue, et al.
Agricultural Systems (2023) Vol. 210, pp. 103711-103711
Closed Access | Times Cited: 55
Winter wheat yield prediction in the conterminous United States using solar-induced chlorophyll fluorescence data and XGBoost and random forest algorithm
Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, et al.
Ecological Informatics (2023) Vol. 77, pp. 102194-102194
Closed Access | Times Cited: 46
Modern computational approaches for rice yield prediction: A systematic review of statistical and machine learning-based methods
Djavan De Clercq, Adam Mahdi
Computers and Electronics in Agriculture (2025) Vol. 231, pp. 109852-109852
Closed Access | Times Cited: 2
Rice Yield Estimation Using Deep Learning
Niyati Mishra, Sushruta Mishra, Hrudaya Kumar Tripathy
Communications in computer and information science (2022), pp. 379-388
Closed Access | Times Cited: 60
Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China
Zhenwang Li, Lei Ding, Dawei Xu
The Science of The Total Environment (2022) Vol. 815, pp. 152880-152880
Closed Access | Times Cited: 54
Accurately mapping global wheat production system using deep learning algorithms
Yuchuan Luo, Zhao Zhang, Juan Cao, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 110, pp. 102823-102823
Open Access | Times Cited: 48
Winter Wheat Yield Prediction Using an LSTM Model from MODIS LAI Products
Jian Wang, Haiping Si, Zhao Gao, et al.
Agriculture (2022) Vol. 12, Iss. 10, pp. 1707-1707
Open Access | Times Cited: 42
Predicting Crop Yield Using Deep Learning and Remote Sensing
Jasmin Praful Bharadiya, Nikolaos Tzenios, Manjunath Reddy
Journal of Engineering Research and Reports (2023) Vol. 24, Iss. 12, pp. 29-44
Open Access | Times Cited: 42
Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges
Khadija Meghraoui, Imane Sebari, Jürgen Pilz, et al.
Technologies (2024) Vol. 12, Iss. 4, pp. 43-43
Open Access | Times Cited: 17
A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations
Jiahao Han, Shibo Fang, Qianchuan Mi, et al.
The Science of The Total Environment (2024) Vol. 914, pp. 169992-169992
Closed Access | Times Cited: 11
Review of deep learning-based methods for non-destructive evaluation of agricultural products
Zhenye Li, Dongyi Wang, Tingting Zhu, et al.
Biosystems Engineering (2024) Vol. 245, pp. 56-83
Closed Access | Times Cited: 11
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
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
CNN-BI-LSTM-CYP: A deep learning approach for sugarcane yield prediction
Preeti Saini, Bharti Nagpal, Puneet Garg, et al.
Sustainable Energy Technologies and Assessments (2023) Vol. 57, pp. 103263-103263
Closed Access | Times Cited: 21
Improved prediction of rice yield at field and county levels by synergistic use of SAR, optical and meteorological data
Weiguo Yu, Gaoxiang Yang, Dong Li, et al.
Agricultural and Forest Meteorology (2023) Vol. 342, pp. 109729-109729
Closed Access | Times Cited: 21
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
Huaqing Wu, Jing Zhang, Zhao Zhang, et al.
Earth system science data (2023) Vol. 15, Iss. 2, pp. 791-808
Open Access | Times Cited: 17
Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery
Hongkui Zhou, Jianhua Yang, Weidong Lou, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 17
Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data
Djavan De Clercq, Adam Mahdi
Agricultural Systems (2024) Vol. 220, pp. 104099-104099
Closed Access | Times Cited: 8
Random Forest for rice yield mapping and prediction using Sentinel-2 data with Google Earth Engine
Komal Choudhary, Wenzhong Shi, Dong Yu, et al.
Advances in Space Research (2022) Vol. 70, Iss. 8, pp. 2443-2457
Closed Access | Times Cited: 28
Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network
Xiaoyan Kang, Changping Huang, Lifu Zhang, et al.
Computers and Electronics in Agriculture (2022) Vol. 201, pp. 107260-107260
Closed Access | Times Cited: 28
Page 1 - Next Page

Scroll to top