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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:

Estimation of soybean yield from machine learning techniques and multispectral RPAS imagery
Fernando Coelho Eugênio, Mara Grohs, Luan Peroni Venâncio, et al.
Remote Sensing Applications Society and Environment (2020) Vol. 20, pp. 100397-100397
Closed Access | Times Cited: 41

Showing 1-25 of 41 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
Improving wheat yield prediction integrating proximal sensing and weather data with machine learning
Guojie Ruan, Xinyu Li, Fei Yuan, et al.
Computers and Electronics in Agriculture (2022) Vol. 195, pp. 106852-106852
Closed Access | Times Cited: 72
Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing
Zhuangzhuang Sun, Qing Li, Shichao Jin, et al.
Plant Phenomics (2022) Vol. 2022
Open Access | Times Cited: 56
Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa
Tunrayo Alabi, Abush Tesfaye, Godfree Chigeza, et al.
Remote Sensing Applications Society and Environment (2022) Vol. 27, pp. 100782-100782
Open Access | Times Cited: 47
Machine learning technology for early prediction of grain yield at the field scale: A systematic review
Joerg Leukel, Tobias Zimpel, Christoph Stumpe
Computers and Electronics in Agriculture (2023) Vol. 207, pp. 107721-107721
Closed Access | Times Cited: 34
Classification of soybean genotypes for industrial traits using UAV multispectral imagery and machine learning
Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 29, pp. 100919-100919
Closed Access | Times Cited: 24
Comparing Machine Learning Algorithms for Estimating the Maize Crop Water Stress Index (CWSI) Using UAV-Acquired Remotely Sensed Data in Smallholder Croplands
Mpho Kapari, Mbulisi Sibanda, James Magidi, et al.
Drones (2024) Vol. 8, Iss. 2, pp. 61-61
Open Access | Times Cited: 14
Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4632-4632
Open Access | Times Cited: 56
A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France
David Camilo Corrales, Céline Schoving, Hélène Raynal, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106578-106578
Open Access | Times Cited: 33
Development of remote sensing-based yield prediction models at the maturity stage of boro rice using parametric and nonparametric approaches
Md. Monirul Islam, Shusuke Matsushita, Ryozo Noguchi, et al.
Remote Sensing Applications Society and Environment (2021) Vol. 22, pp. 100494-100494
Closed Access | Times Cited: 30
Grain Crop Yield Prediction Using Machine Learning Based on UAV Remote Sensing: A Systematic Literature Review
Jianghao Yuan, Yangliang Zhang, Zuojun Zheng, et al.
Drones (2024) Vol. 8, Iss. 10, pp. 559-559
Open Access | Times Cited: 4
Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management
Fábio Henrique Rojo Baio, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, et al.
Remote Sensing (2022) Vol. 15, Iss. 1, pp. 79-79
Open Access | Times Cited: 18
Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning
Shanxin Zhang, Hao Feng, Shaoyu Han, et al.
Agriculture (2022) Vol. 13, Iss. 1, pp. 110-110
Open Access | Times Cited: 18
Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
Ricardo Gava, Dthenifer Cordeiro Santana, Mayara Fávero Cotrim, et al.
Sustainability (2022) Vol. 14, Iss. 12, pp. 7125-7125
Open Access | Times Cited: 17
Estimated flooded rice grain yield and nitrogen content in leaves based on RPAS images and machine learning
Fernando Coelho Eugênio, Mara Grohs, Mateus Sabadi Schuh, et al.
Field Crops Research (2023) Vol. 292, pp. 108823-108823
Closed Access | Times Cited: 10
Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning
Kamand Bagherian, Rafael Bidese‐Puhl, Yin Bao, et al.
The Plant Phenome Journal (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 10
Assessment of the Maize Crop Water Stress Index (CWSI) Using Drone-Acquired Data Across Different Phenological Stages
Mpho Kapari, Mbulisi Sibanda, James Magidi, et al.
Drones (2025) Vol. 9, Iss. 3, pp. 192-192
Open Access
Analyzing different phenotypic methods of soybean leaves under the high temperature stress with near-infrared spectroscopy, microscopic Image, and multispectral image
Ying Deng, Weizhi Yang, Jiajia Li, et al.
Computers and Electronics in Agriculture (2025) Vol. 234, pp. 110281-110281
Closed Access
Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning
Longyu Zhou, Dezhi Han, Guangyao Sun, et al.
Plant Phenomics (2025), pp. 100028-100028
Open Access
UAV-based spectral images using remote sensing and YOLOv8 in <i>Eucalyptus saligna</i> Sm. inventory
Vinícius Richter, Max Vinícios Reis de Sousa, Rodrigo Thirion Correia dos Santos, et al.
Ciência Florestal (2025), pp. e88522-e88522
Open Access
Flooded rice variables from high-resolution multispectral images and machine learning algorithms
Fernando Coelho Eugênio, Mara Grohs, Mateus Sabadi Schuh, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 31, pp. 100998-100998
Closed Access | Times Cited: 6
A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications
Jithin Mathew, Nadia Delavarpour, Carrie Miranda, et al.
Sensors (2023) Vol. 23, Iss. 14, pp. 6506-6506
Open Access | Times Cited: 6
Multilayer optimized deep learning model to analyze spectral indices for predicting the condition of rice blast disease
Shubhajyoti Das, Pritam Bikram, Arindam Biswas, et al.
Remote Sensing Applications Society and Environment (2024), pp. 101394-101394
Closed Access | Times Cited: 2
Machine Learning for Smart Agriculture: A Comprehensive Survey
M. Rezwanul Mahmood, M. A. Matin, Sotirios K. Goudos, et al.
IEEE Transactions on Artificial Intelligence (2023) Vol. 5, Iss. 6, pp. 2568-2588
Closed Access | Times Cited: 4
Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles
Ghulam Mustafa, Y H Liu, İmran Khan, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 1
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