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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:

Employing artificial neural network for effective biomass prediction: An alternative approach
Şükrü Teoman Güner, Maria J. Diamantopoulou, Krishna P. Poudel, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106596-106596
Closed Access | Times Cited: 25

Showing 25 citing articles:

Estimating vertically growing crop above-ground biomass based on UAV remote sensing
Jibo Yue, Hao Yang, Guijun Yang, et al.
Computers and Electronics in Agriculture (2023) Vol. 205, pp. 107627-107627
Closed Access | Times Cited: 61
Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region, Türkiye
Sinan Bulut
Ecological Informatics (2022) Vol. 74, pp. 101951-101951
Closed Access | Times Cited: 25
The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests
Yiru Zhang, Binbin He, Rui Chen, et al.
GIScience & Remote Sensing (2024) Vol. 61, Iss. 1
Open Access | Times Cited: 4
Recent advancements in biomass to bioenergy management and carbon capture through artificial intelligence integrated technologies to achieve carbon neutrality
Shivani Chauhan, Preeti Solanki, Chayanika Putatunda, et al.
Sustainable Energy Technologies and Assessments (2024) Vol. 73, pp. 104123-104123
Closed Access | Times Cited: 4
Application of Machine Learning for Aboveground Biomass Modeling in Tropical and Temperate Forests from Airborne Hyperspectral Imagery
Patrick Osei Darko, Samy Metari, J. Pablo Arroyo‐Mora, et al.
Forests (2025) Vol. 16, Iss. 3, pp. 477-477
Open Access
Forest Aboveground Biomass Estimation in Küre Mountains National Park Using Multifrequency SAR and Multispectral Optical Data with Machine-Learning Regression Models
Eren Gürsoy ÖZDEMİR, Saygın Abdikan
Remote Sensing (2025) Vol. 17, Iss. 6, pp. 1063-1063
Open Access
Simultaneous density-integral system for estimating stem profile, volume, wood density, and biomass of fourteen tree species in northeast China
Zipeng Zhang, Xiangwei Yang, Pei He, et al.
Computers and Electronics in Agriculture (2025) Vol. 236, pp. 110453-110453
Closed Access
Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests
Thuy Phuong Nguyen, Phuc Khoa Nguyen, Huu Ngu Nguyen, et al.
Forest Science and Technology (2024), pp. 1-13
Open Access | Times Cited: 3
Estimating Stratified Biomass in Cotton Fields Using UAV Multispectral Remote Sensing and Machine Learning
Zhicheng Hu, Shiyu Fan, Yabin Li, et al.
Drones (2025) Vol. 9, Iss. 3, pp. 186-186
Open Access
Developing a comprehensive evaluation model of variety adaptability based on machine learning method
Yanyun Han, Kaiyi Wang, Qi Zhang, et al.
Field Crops Research (2023) Vol. 306, pp. 109203-109203
Closed Access | Times Cited: 7
Biomass Prediction Using Sentinel-2 Imagery and an Artificial Neural Network in the Amazon/Cerrado Transition Region
Luana Duarte de Faria, Eraldo Aparecido Trondoli Matricardi, Beatriz Schwantes Marimon, et al.
Forests (2024) Vol. 15, Iss. 9, pp. 1599-1599
Open Access | Times Cited: 2
Application of pharmaceutical waste as a heterogeneous catalyst for transesterification of waste cooking oil: biofuel production and its modeling using predictive tools
Ramin Tahmasebi-Boldaji, Saman Rashidi, Hossein Rajabi Kuyakhi, et al.
Biofuels (2023) Vol. 15, Iss. 4, pp. 415-431
Closed Access | Times Cited: 6
Parametric Optimization of Microhardness of Electroless Ni-Zn-Cu-P Coating Using Taguchi Design and Artificial Neural Network
Chandra Sekhar Rauta, Gautam Majumdar, Sandip Sarkar
JOM (2022) Vol. 74, Iss. 12, pp. 4564-4574
Closed Access | Times Cited: 8
Multi-output deep learning models for enhanced reliability of simultaneous tree above- and below-ground biomass predictions in tropical forests of Vietnam
Bao Huy, Nguyen Quy Truong, Krishna P. Poudel, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109080-109080
Closed Access | Times Cited: 1
Orman ekosistemindeki ağaç boylarının, optik, radar, lazer altimetre uydu verileri ve yardımcı kaynaklar kullanılarak Google Earth Engine platformunda modellenmesi
Eren Gürsoy ÖZDEMİR, Tarık Utku Zengin, Halit Abdullah Güleç
Geomatik (2024) Vol. 9, Iss. 2, pp. 259-268
Open Access | Times Cited: 1
Machine learning and woody biomasses: Assessing wood chip quality for sustainable energy production
Thomas Gasperini, Volkan Yeşil, Giuseppe Toscano
Biomass and Bioenergy (2024) Vol. 193, pp. 107527-107527
Open Access | Times Cited: 1
Comparative analysis of machine learning algorithms and statistical models for predicting crown width of Larix olgensis
Siyu Qiu, Ruiting Liang, Yifu Wang, et al.
Earth Science Informatics (2022) Vol. 15, Iss. 4, pp. 2415-2429
Closed Access | Times Cited: 5
Some Modified Activation Functions of Hyperbolic Tangent (TanH) Activation Function for Artificial Neural Networks
Arvind Kumar, Sartaj Singh Sodhi
Advances in intelligent systems and computing (2023), pp. 369-392
Closed Access | Times Cited: 2
An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China
Shidong Xin, Muhammad Khurram Shahzad, Surya Bagus Mahardika, et al.
Forests (2023) Vol. 14, Iss. 6, pp. 1274-1274
Open Access | Times Cited: 2
Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm
Maria J. Diamantopoulou
Folia oecologica (2022) Vol. 49, Iss. 2, pp. 93-101
Open Access | Times Cited: 4
Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches
Şerife KALKANLI, Maria J. Diamantopoulou, Ramazan Özçelík
Forests (2023) Vol. 14, Iss. 12, pp. 2429-2429
Open Access | Times Cited: 2
Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees
MJ Diamantopoulou, Aydın Çömez, Ramazan Özçelík, et al.
iForest - Biogeosciences and Forestry (2024) Vol. 17, Iss. 1, pp. 19-28
Open Access
An additive model system for heartwood, sapwood and bark diameter – A working example in Pinus koraiensis Siebold & Zucc. plantations
Yuman Sun, Weiwei Jia, Subati Saidahemaiti
Computers and Electronics in Agriculture (2024) Vol. 220, pp. 108868-108868
Closed Access
Constructing and Validating Estimation Models for Individual-Tree Aboveground Biomass of Salix suchowensis in China
Wei Fu, Chaoyue Niu, Chuanjing Hu, et al.
Forests (2024) Vol. 15, Iss. 8, pp. 1371-1371
Open Access
A Compatible Estimation Method for Biomass Factors Based on Allometric Relationship: A Case Study on Pinus densata Natural Forest in Yunnan Province of Southwest China
Wenfang Li, Hui Xu, Yong Wu, et al.
Forests (2023) Vol. 15, Iss. 1, pp. 26-26
Open Access | Times Cited: 1
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