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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:

Field phenotyping of plant height in an upland rice field in Laos using low-cost small unmanned aerial vehicles (UAVs)
Kensuke Kawamura, Hidetoshi Asai, Taisuke Yasuda, et al.
Plant Production Science (2020) Vol. 23, Iss. 4, pp. 452-465
Open Access | Times Cited: 64

Showing 1-25 of 64 citing articles:

Applications of hyperspectral imaging in plant phenotyping
Rijad Sarić, Viet Duc Nguyen, Timothy Burge, et al.
Trends in Plant Science (2022) Vol. 27, Iss. 3, pp. 301-315
Closed Access | Times Cited: 151
Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches
Xinbin Zhou, Yusuke Kono, Aung Naing Win, et al.
Plant Production Science (2020) Vol. 24, Iss. 2, pp. 137-151
Open Access | Times Cited: 108
Unmanned Aerial Vehicle-Based Photogrammetric 3D Mapping: A survey of techniques, applications, and challenges
San Jiang, Wanshou Jiang, Lizhe Wang
IEEE Geoscience and Remote Sensing Magazine (2021) Vol. 10, Iss. 2, pp. 135-171
Closed Access | Times Cited: 65
High-throughput field crop phenotyping: current status and challenges
S. Ninomiya
Breeding Science (2022) Vol. 72, Iss. 1, pp. 3-18
Open Access | Times Cited: 39
Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses
Ting‐Chi Wen, Jianhong Li, Qi Wang, et al.
The Science of The Total Environment (2023) Vol. 899, pp. 165626-165626
Closed Access | Times Cited: 24
Dos and Don'ts of using drone technology in the crop fields
Jamileh Aliloo, Enayat Abbasi, Esmail Karamidehkordi, et al.
Technology in Society (2024) Vol. 76, pp. 102456-102456
Closed Access | Times Cited: 14
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
Discrete Element Method Analysis of Soil Penetration Depth Affected by Spreading Speed in Drone-Seeded Rice
Kwon Son
Agriculture (2025) Vol. 15, Iss. 4, pp. 422-422
Open Access | Times Cited: 1
Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm
Kensuke Kawamura, Hidetoshi Asai, Taisuke Yasuda, et al.
Plant Production Science (2020) Vol. 24, Iss. 2, pp. 198-215
Open Access | Times Cited: 47
Monitoring spatial and time-series variations in red crown rot damage of soybean in farmer fields based on UAV remote sensing
Shuhei Yamamoto, S Nomoto, Naoyuki Hashimoto, et al.
Plant Production Science (2023) Vol. 26, Iss. 1, pp. 36-47
Open Access | Times Cited: 14
Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing
Jingrong Zang, Shichao Jin, Songyin Zhang, et al.
Plant Methods (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 14
High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing
Yuta Matsuura, Heming Zhang, Kousuke Nakao, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 14
Evaluation of Sugarcane Crop Growth Monitoring Using Vegetation Indices Derived from RGB-Based UAV Images and Machine Learning Models
P. P. Ruwanpathirana, Kazuhito Sakai, Guttila Yugantha Jayasinghe, et al.
Agronomy (2024) Vol. 14, Iss. 9, pp. 2059-2059
Open Access | Times Cited: 6
UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits
Farrah Melissa Muharam, Khairudin Nurulhuda, Zed Zulkafli, et al.
Agronomy (2021) Vol. 11, Iss. 5, pp. 915-915
Open Access | Times Cited: 29
UAV time-series imagery show diversity treatment effects on cabbage growth
Norazlida Jamil, Gert Kootstra, Dirk F. van Apeldoorn, et al.
Smart Agricultural Technology (2024) Vol. 8, pp. 100443-100443
Open Access | Times Cited: 4
Breeding rice for a changing climate by improving adaptations to water saving technologies
Maria Cristina Heredia, Josefine Kant, M. Asaduzzaman Prodhan, et al.
Theoretical and Applied Genetics (2021) Vol. 135, Iss. 1, pp. 17-33
Closed Access | Times Cited: 25
Advances in Crop Improvement through High-Throughput Phenotyping
J Padhy, Ratan Sarkar, Sourav Ranjan Mohapatra, et al.
CABI eBooks (2025), pp. 194-215
Closed Access
Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing
Anish Bhattarai, Gonzalo Joel Scarpin, Amrinder Jakhar, et al.
Remote Sensing (2025) Vol. 17, Iss. 9, pp. 1504-1504
Open Access
Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management
Chin-Ying Yang, Ming‐Der Yang, Wei-Cheng Tseng, et al.
Sensors (2020) Vol. 20, Iss. 18, pp. 5354-5354
Open Access | Times Cited: 27
Evaluation of Individual Plant Growth Estimation in an Intercropping Field with UAV Imagery
Norazlida Jamil, Gert Kootstra, Lammert Kooistra
Agriculture (2022) Vol. 12, Iss. 1, pp. 102-102
Open Access | Times Cited: 15
Comparison of Remote Sensing Methods for Plant Heights in Agricultural Fields Using Unmanned Aerial Vehicle-Based Structure From Motion
Ryo Fujiwara, Tomohiro Kikawada, Hisashi Sato, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 15
Genome-wide association studies: an intuitive solution for SNP identification and gene mapping in trees
M. N. Ashwath, Shivaji Ajinath Lavale, A. V. Santhoshkumar, et al.
Functional & Integrative Genomics (2023) Vol. 23, Iss. 4
Closed Access | Times Cited: 9
Assessing the Self-Recovery Ability of Maize after Lodging Using UAV-LiDAR Data
Xueqian Hu, Lin Sun, Xiaohe Gu, et al.
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2270-2270
Open Access | Times Cited: 19
Assessment of grass lodging using texture and canopy height distribution features derived from UAV visual-band images
Suiyan Tan, Anders Krogh Mortensen, Xu Ma, et al.
Agricultural and Forest Meteorology (2021) Vol. 308-309, pp. 108541-108541
Closed Access | Times Cited: 19
Spatio-Temporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images
Clement Oppong Peprah, M. Yamashita, Tomoaki Yamaguchi, et al.
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2388-2388
Open Access | Times Cited: 18
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