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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:

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery
Yaxiao Niu, Liyuan Zhang, Huihui Zhang, et al.
Remote Sensing (2019) Vol. 11, Iss. 11, pp. 1261-1261
Open Access | Times Cited: 149

Showing 1-25 of 149 citing articles:

Computer vision technology in agricultural automation —A review
Hongkun Tian, Tianhai Wang, Yadong Liu, et al.
Information Processing in Agriculture (2019) Vol. 7, Iss. 1, pp. 1-19
Open Access | Times Cited: 517
The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems
Jinha Jung, Murilo Maeda, Anjin Chang, et al.
Current Opinion in Biotechnology (2020) Vol. 70, pp. 15-22
Open Access | Times Cited: 332
Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects
Jiachen Yang, Chenguang Wang, Bin Jiang, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 3, pp. 2204-2219
Open Access | Times Cited: 205
Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107089-107089
Closed Access | Times Cited: 100
Enabling smart agriculture by implementing artificial intelligence and embedded sensing
Ashutosh Sharma, Mikhail Georgi, Maxim D. Tregubenko, et al.
Computers & Industrial Engineering (2022) Vol. 165, pp. 107936-107936
Closed Access | Times Cited: 79
Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 72
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
Harrison Kurunathan, Hailong Huang, Kai Li, et al.
IEEE Communications Surveys & Tutorials (2023) Vol. 26, Iss. 1, pp. 496-533
Open Access | Times Cited: 71
Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2023) Vol. 213, pp. 108229-108229
Closed Access | Times Cited: 64
Precision agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption
Marcelo Rodrigues Barbosa Júnior, Bruno Rafael de Almeida Moreira, Vinicius dos Santos Carreira, et al.
Computers and Electronics in Agriculture (2024) Vol. 221, pp. 108993-108993
Closed Access | Times Cited: 32
Crop canopy volume weighted by color parameters from UAV-based RGB imagery to estimate above-ground biomass of potatoes
Yang Liu, Fuqin Yang, Jibo Yue, et al.
Computers and Electronics in Agriculture (2024) Vol. 227, pp. 109678-109678
Closed Access | Times Cited: 18
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
FIELDimageR: An R package to analyze orthomosaic images from agricultural field trials
Filipe Inácio Matias, Maria V. Caraza‐Harter, Jeffrey B. Endelman
The Plant Phenome Journal (2020) Vol. 3, Iss. 1
Open Access | Times Cited: 110
Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize
Junsheng Lu, Dongling Cheng, Chenming Geng, et al.
Biosystems Engineering (2020) Vol. 202, pp. 42-54
Closed Access | Times Cited: 106
Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
Juanjuan Zhang, Tao Cheng, Wei Guo, et al.
Plant Methods (2021) Vol. 17, Iss. 1
Open Access | Times Cited: 105
Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data
Longfei Zhou, Xiaohe Gu, Shu Cheng, et al.
Agriculture (2020) Vol. 10, Iss. 5, pp. 146-146
Open Access | Times Cited: 91
Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
Botao Chen, Xidong Mu, Peng Chen, et al.
Ecological Indicators (2021) Vol. 133, pp. 108434-108434
Open Access | Times Cited: 87
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
State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations
Mohamad Hanif Md Saad, Nurul Maisarah Hamdan, Mahidur R. Sarker
Electronics (2021) Vol. 10, Iss. 12, pp. 1422-1422
Open Access | Times Cited: 81
Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods
Yahui Guo, Guodong Yin, Hongyong Sun, et al.
Sensors (2020) Vol. 20, Iss. 18, pp. 5130-5130
Open Access | Times Cited: 78
Crop height estimation based on UAV images: Methods, errors, and strategies
Tianjin Xie, Jijun Li, Chenghai Yang, et al.
Computers and Electronics in Agriculture (2021) Vol. 185, pp. 106155-106155
Closed Access | Times Cited: 69
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
The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing
Bin Yang, Wanxue Zhu, Ehsan Eyshi Rezaei, et al.
Remote Sensing (2022) Vol. 14, Iss. 7, pp. 1559-1559
Open Access | Times Cited: 46
Improved estimation of aboveground biomass in rubber plantations by fusing spectral and textural information from UAV-based RGB imagery
Yuying Liang, Weili Kou, Hongyan Lai, et al.
Ecological Indicators (2022) Vol. 142, pp. 109286-109286
Closed Access | Times Cited: 46
Using the plant height and canopy coverage to estimation maize aboveground biomass with UAV digital images
Meiyan Shu, Qing Li, Abu Zar Ghafoor, et al.
European Journal of Agronomy (2023) Vol. 151, pp. 126957-126957
Closed Access | Times Cited: 39
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