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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:

Autumn Crop Yield Prediction using Data-Driven Approaches:- Support Vector Machines, Random Forest, and Deep Neural Network Methods
Chaoya Dang, Ying Liu, Hui Yue, et al.
Canadian Journal of Remote Sensing (2020) Vol. 47, Iss. 2, pp. 162-181
Closed Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Machine Learning in Agriculture: A Comprehensive Updated Review
Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, et al.
Sensors (2021) Vol. 21, Iss. 11, pp. 3758-3758
Open Access | Times Cited: 526
Smart farming using artificial intelligence: A review
Yaganteeswarudu Akkem, Saroj Kr. Biswas, Aruna Varanasi
Engineering Applications of Artificial Intelligence (2023) Vol. 120, pp. 105899-105899
Closed Access | Times Cited: 218
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: 181
Remote Sensing in Field Crop Monitoring: A Comprehensive Review of Sensor Systems, Data Analyses and Recent Advances
Emmanuel Omia, Hyungjin Bae, Eunsung Park, et al.
Remote Sensing (2023) Vol. 15, Iss. 2, pp. 354-354
Open Access | Times Cited: 121
Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review
Aya Ferchichi, Ali Ben Abbes, Vincent Barra, et al.
Ecological Informatics (2022) Vol. 68, pp. 101552-101552
Closed Access | Times Cited: 90
10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product
Khuong H. Tran, Hankui K. Zhang, John McMaine, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 107, pp. 102692-102692
Open Access | Times Cited: 74
Classification and yield prediction in smart agriculture system using IoT
Akanksha Gupta, Priyank Nahar
Journal of Ambient Intelligence and Humanized Computing (2022) Vol. 14, Iss. 8, pp. 10235-10244
Open Access | Times Cited: 59
Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia
Alexis Pang, Melissa W L Chang, Yang Chen
Sensors (2022) Vol. 22, Iss. 3, pp. 717-717
Open Access | Times Cited: 46
A data-driven crop model for maize yield prediction
Yanbin Chang, Jeremy Latham, Mark A. Licht, et al.
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 33
Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices
Mojtaba Naghdyzadegan Jahromi, Shahrokh Zand‐Parsa, Fatemeh Razzaghi, et al.
European Journal of Agronomy (2023) Vol. 146, pp. 126820-126820
Closed Access | Times Cited: 29
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: 17
UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning
Vikas Sharma, Eija Honkavaara, Matthew Hayden, et al.
Plant Stress (2024) Vol. 12, pp. 100464-100464
Open Access | Times Cited: 12
An efficient IoT-based crop yield prediction framework using optimal ensemble learning and hybridized optimization model
K. Ravi Shankar, M. Moorthi
Earth Science Informatics (2025) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
Deep neural network and transfer learning for annual wetland vegetation mapping using sentinel-2 time-series data in the heterogeneous lake floodplain environment
Jinquan Ai, Xinxing Han, Lijuan Chen, et al.
International Journal of Remote Sensing (2025), pp. 1-24
Closed Access | Times Cited: 1
Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan
John Karongo, Joseph Ivivi Mwaniki, John Ndiritu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
An integrated artificial intelligence-deep learning approach for vegetation canopy assessment and monitoring through satellite images
Nazila Shamloo, Mohammad Taghi Sattari, Khalil Valizadeh Kamran, et al.
Stochastic Environmental Research and Risk Assessment (2025)
Closed Access | Times Cited: 1
Prediction and Spatiotemporal Dynamics of Vegetation Index Based on Deep Learning and Environmental Factors in the Yangtze River Basin
Yin Wang, Nan Zhang, Mingjie Chen, et al.
Forests (2025) Vol. 16, Iss. 3, pp. 460-460
Open Access | Times Cited: 1
Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms
Kudzai Shaun Mpakairi, Timothy Dube, Mbulisi Sibanda, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 204, pp. 117-130
Open Access | Times Cited: 23
Utilization of synthetic minority oversampling technique for improving potato yield prediction using remote sensing data and machine learning algorithms with small sample size of yield data
Hamid Ebrahimy, Yi Wang, Zhou Zhang
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 201, pp. 12-25
Open Access | Times Cited: 20
Yield estimation from SAR data using patch-based deep learning and machine learning techniques
Mahya G.Z. Hashemi, Pang‐Ning Tan, Ehsan Jalilvand, et al.
Computers and Electronics in Agriculture (2024) Vol. 226, pp. 109340-109340
Closed Access | Times Cited: 8
Multi-source information fusion-driven corn yield prediction using the Random Forest from the perspective of Agricultural and Forestry Economic Management
Xuziqi Yang, Zekai Hua, Liang Li, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Enhanced drought detection and monitoring using sun-induced chlorophyll fluorescence over Hulun Buir Grassland, China
Ying Liu, Chaoya Dang, Hui Yue, et al.
The Science of The Total Environment (2021) Vol. 770, pp. 145271-145271
Closed Access | Times Cited: 38
Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review
Srinivasagan N. Subhashree, C. Igathinathane, Adnan Akyüz, et al.
Agriculture (2023) Vol. 13, Iss. 2, pp. 455-455
Open Access | Times Cited: 16
Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture
Mohammad Aldossary, Hatem A. Alharbi, Ch Anwar Ul Hassan
IEEE Access (2024) Vol. 12, pp. 75718-75734
Open Access | Times Cited: 6
Crop yield prediction based on reanalysis and crop phenology data in the agroclimatic zones
Serhan Yeşilköy, İbrahim Demir
Theoretical and Applied Climatology (2024) Vol. 155, Iss. 7, pp. 7035-7048
Closed Access | Times Cited: 5
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