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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:

Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest
Kasper Johansen, Mitchell J. L. Morton, Yoann Malbéteau, et al.
Frontiers in Artificial Intelligence (2020) Vol. 3
Open Access | Times Cited: 85

Showing 1-25 of 85 citing articles:

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses
Muhammet Fatih Aslan, Akif Durdu, Kadir Sabancı, et al.
Applied Sciences (2022) Vol. 12, Iss. 3, pp. 1047-1047
Open Access | Times Cited: 170
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
Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review
Boubacar Gano, Sourav Bhadra, Justin M. Vilbig, et al.
The Plant Phenome Journal (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 20
Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data Integration
Maryam Abbasi, Paulo Váz, José Silva, et al.
Sustainability (2025) Vol. 17, Iss. 1, pp. 256-256
Open Access | Times Cited: 2
Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data
Shezhou Luo, Weiwei Liu, Yaqian Zhang, et al.
Computers and Electronics in Agriculture (2021) Vol. 182, pp. 106005-106005
Closed Access | Times Cited: 71
Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China
Minghan Cheng, Josep Peñuelas, Matthew F. McCabe, et al.
Agricultural and Forest Meteorology (2022) Vol. 323, pp. 109057-109057
Closed Access | Times Cited: 62
Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach
Jiale Jiang, Kasper Johansen, Clara S. Stanschewski, et al.
Precision Agriculture (2022) Vol. 23, Iss. 3, pp. 961-983
Open Access | Times Cited: 57
Multi-sensor and multi-platform consistency and interoperability between UAV, Planet CubeSat, Sentinel-2, and Landsat reflectance data
Jiale Jiang, Kasper Johansen, Yu-Hsuan Tu, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 936-958
Open Access | Times Cited: 52
High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
Minghan Cheng, Xiyun Jiao, Lei Shi, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 41
Capturing crop adaptation to abiotic stress using image-based technologies
Nadia Al‐Tamimi, Patrick Langan, Villő Bernád, et al.
Open Biology (2022) Vol. 12, Iss. 6
Open Access | Times Cited: 39
Image-Based High-Throughput Phenotyping in Horticultural Crops
Alebel Mekuriaw Abebe, Younguk Kim, Jae-Young Kim, et al.
Plants (2023) Vol. 12, Iss. 10, pp. 2061-2061
Open Access | Times Cited: 39
UAV image acquisition and processing for high‐throughput phenotyping in agricultural research and breeding programs
Ocident Bongomin, Jimmy Lamo, Joshua Mugeziaubwa Guina, et al.
The Plant Phenome Journal (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 12
Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images
Simone Pietro Garofalo, Vincenzo Giannico, Beatriz Lorente, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 11
An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower
Luana Centorame, Thomas Gasperini, Alessio Ilari, et al.
Agronomy (2024) Vol. 14, Iss. 4, pp. 719-719
Open Access | Times Cited: 9
Garlic yield monitoring using vegetation indices and texture features derived from UAV multispectral imagery
Andrea Marcone, Giorgio Impollonia, Michele Croci, et al.
Smart Agricultural Technology (2024) Vol. 8, pp. 100513-100513
Open Access | Times Cited: 9
Enhancing snap bean yield prediction through synergistic integration of UAS-Based LiDAR and multispectral imagery
Fei Zhang, Amirhossein Hassanzadeh, Peter Letendre, et al.
Computers and Electronics in Agriculture (2025) Vol. 230, pp. 109923-109923
Open Access | Times Cited: 2
Quinoa Phenotyping Methodologies: An International Consensus
Clara S. Stanschewski, Elodie Rey, Gabriele Fiene, et al.
Plants (2021) Vol. 10, Iss. 9, pp. 1759-1759
Open Access | Times Cited: 55
Deep Learning for Strawberry Canopy Delineation and Biomass Prediction from High-Resolution Images
Caiwang Zheng, Amr Abd‐Elrahman, Vance M. Whitaker, et al.
Plant Phenomics (2022) Vol. 2022
Open Access | Times Cited: 35
Estimating Yield-Related Traits Using UAV-Derived Multispectral Images to Improve Rice Grain Yield Prediction
Maria Victoria Bascon, Tomohiro Nakata, Satoshi Shibata, et al.
Agriculture (2022) Vol. 12, Iss. 8, pp. 1141-1141
Open Access | Times Cited: 30
A Proposed Ensemble Feature Selection Method for Estimating Forest Aboveground Biomass from Multiple Satellite Data
Yuzhen Zhang, Jingjing Liu, Wenhao Li, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 1096-1096
Open Access | Times Cited: 16
Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods
Mingzheng Zhang, Tianen Chen, Xiaohe Gu, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 15
Evaluating how lodging affects maize yield estimation based on UAV observations
Yuan Liu, Chenwei Nie, Zhen Zhang, et al.
Frontiers in Plant Science (2023) Vol. 13
Open Access | Times Cited: 14
An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass
Aliasghar Bazrafkan, Nadia Delavarpour, Peter G. Oduor, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3543-3543
Open Access | Times Cited: 14
Integration of Unmanned Aerial Vehicle Spectral and Textural Features for Accurate Above-Ground Biomass Estimation in Cotton
M Ingjun Chen, Caixia Yin, Tao Lin, et al.
Agronomy (2024) Vol. 14, Iss. 6, pp. 1313-1313
Open Access | Times Cited: 6
Revitalizing agriculture: next-generation genotyping and -omics technologies enabling molecular prediction of resilient traits in the Solanaceae family
Matteo Martina, Valeria De Rosa, Gabriele Magon, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 5
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