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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:

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features
Qi Jiang, Shenghui Fang, Yi Peng, et al.
Remote Sensing (2019) Vol. 11, Iss. 7, pp. 890-890
Open Access | Times Cited: 82

Showing 1-25 of 82 citing articles:

Applications of machine vision in agricultural robot navigation: A review
Tianhai Wang, Bao‐Ji Chen, Zhenqian Zhang, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107085-107085
Closed Access | Times Cited: 139
Estimating potato above-ground biomass based on vegetation indices and texture features constructed from sensitive bands of UAV hyperspectral imagery
Yang Liu, Yiguang Fan, Haikuan Feng, et al.
Computers and Electronics in Agriculture (2024) Vol. 220, pp. 108918-108918
Closed Access | Times Cited: 55
Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108808-108808
Closed Access | Times Cited: 48
A model suitable for estimating above-ground biomass of potatoes at different regional levels
Yang Liu, Yiguang Fan, Jibo Yue, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109081-109081
Closed Access | Times Cited: 34
A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems
Lucy G. Poley, Gregory J. McDermid
Remote Sensing (2020) Vol. 12, Iss. 7, pp. 1052-1052
Open Access | Times Cited: 120
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
Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season
Yan Gong, Kaili Yang, Zhiheng Lin, et al.
Plant Methods (2021) Vol. 17, Iss. 1
Open Access | Times Cited: 67
Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV
Falv Wang, Mao Yang, Longfei Ma, et al.
Remote Sensing (2022) Vol. 14, Iss. 5, pp. 1251-1251
Open Access | Times Cited: 60
Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices
Haibo Yang, Fei Li, Wei Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2339-2339
Open Access | Times Cited: 58
Estimating the maize above-ground biomass by constructing the tridimensional concept model based on UAV-based digital and multi-spectral images
Meiyan Shu, Mengyuan Shen, Dong Qizhou, et al.
Field Crops Research (2022) Vol. 282, pp. 108491-108491
Closed Access | Times Cited: 58
Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits
Meiyan Shu, Shuaipeng Fei, Bingyu Zhang, et al.
Plant Phenomics (2022) Vol. 2022
Open Access | Times Cited: 57
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
Advancing soybean biomass estimation through multi-source UAV data fusion and machine learning algorithms
Haitao Da, Yaxin Li, Le Xu, et al.
Smart Agricultural Technology (2025) Vol. 10, pp. 100778-100778
Open Access | Times Cited: 1
An Internet of Things assisted Unmanned Aerial Vehicle based artificial intelligence model for rice pest detection
Sourav Kumar Bhoi, Kalyan Kumar Jena, Sanjaya Kumar Panda, et al.
Microprocessors and Microsystems (2020) Vol. 80, pp. 103607-103607
Closed Access | Times Cited: 65
Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season
Kaili Yang, Yan Gong, Shenghui Fang, et al.
Remote Sensing (2021) Vol. 13, Iss. 15, pp. 3001-3001
Open Access | Times Cited: 53
Applications of UAS in Crop Biomass Monitoring: A Review
Tianhai Wang, Yadong Liu, Minghui Wang, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 51
Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning
Sebastián Varela, Taylor Pederson, Carl J. Bernacchi, et al.
Remote Sensing (2021) Vol. 13, Iss. 9, pp. 1763-1763
Open Access | Times Cited: 46
Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands
Xueqin Jiang, Shenghui Fang, Xia Huang, et al.
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 579-579
Open Access | Times Cited: 42
Estimating pasture aboveground biomass under an integrated crop-livestock system based on spectral and texture measures derived from UAV images
Rodrigo Greggio de Freitas, Francisco R. da S. Pereira, Aliny Aparecida dos Reis, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107122-107122
Closed Access | Times Cited: 35
Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop
Worasit Sangjan, Rebecca J. McGee, Sindhuja Sankaran
Remote Sensing (2022) Vol. 14, Iss. 10, pp. 2396-2396
Open Access | Times Cited: 29
Estimation of Rice Aboveground Biomass by Combining Canopy Spectral Reflectance and Unmanned Aerial Vehicle-Based Red Green Blue Imagery Data
Zhonglin Wang, Yangming Ma, Ping Chen, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 29
Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery
Shanjun Luo, Xueqin Jiang, Yingbin He, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 29
Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models
Kaili Yang, Jiacai Mo, Shanjun Luo, et al.
Plant Phenomics (2023) Vol. 5
Open Access | Times Cited: 19
Structure from Motion Point Clouds for Structural Monitoring
Fausto Mistretta, G. Sanna, Flavio Stochino, et al.
Remote Sensing (2019) Vol. 11, Iss. 16, pp. 1940-1940
Open Access | Times Cited: 43
Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery
Kenichi Tatsumi, Noa Igarashi, Xiao Mengxue
Plant Methods (2021) Vol. 17, Iss. 1
Open Access | Times Cited: 35
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