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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 Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales
Wanxue Zhu, Zhigang Sun, Jinbang Peng, et al.
Remote Sensing (2019) Vol. 11, Iss. 22, pp. 2678-2678
Open Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Support Vector Machine in Precision Agriculture: A review
Zhi Hong Kok, Abdul Rashid Mohamed Shariff, Meftah Salem M. Alfatni, et al.
Computers and Electronics in Agriculture (2021) Vol. 191, pp. 106546-106546
Closed Access | Times Cited: 143
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
Improved potato AGB estimates based on UAV RGB and hyperspectral images
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2023) Vol. 214, pp. 108260-108260
Closed Access | Times Cited: 68
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
Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales
Wanxue Zhu, Zhigang Sun, Ting Yang, et al.
Computers and Electronics in Agriculture (2020) Vol. 178, pp. 105786-105786
Closed Access | Times Cited: 92
Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV
Yahui Guo, Hanxi Wang, Zhaofei Wu, et al.
Sensors (2020) Vol. 20, Iss. 18, pp. 5055-5055
Open Access | Times Cited: 86
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
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
An improved approach to estimate ratoon rice aboveground biomass by integrating UAV-based spectral, textural and structural features
Le Xu, Longfei Zhou, Ran Meng, et al.
Precision Agriculture (2022) Vol. 23, Iss. 4, pp. 1276-1301
Closed Access | Times Cited: 52
Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms
Danyang Yu, Yuanyuan Zha, Zhigang Sun, et al.
Precision Agriculture (2022) Vol. 24, Iss. 1, pp. 92-113
Closed Access | Times Cited: 46
Enhancing assessment of corn growth performance using unmanned aerial vehicles (UAVs) and deep learning
Juan Xiao, Stanley Anak Suab, Xinyu Chen, et al.
Measurement (2023) Vol. 214, pp. 112764-112764
Closed Access | Times Cited: 34
A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture
Sheikh Muhammad Farhan, Jianjun Yin, Zhijian Chen, et al.
Sensors (2024) Vol. 24, Iss. 16, pp. 5409-5409
Open Access | Times Cited: 13
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
Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques
Liying Geng, Tao Che, Mingguo Ma, et al.
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2352-2352
Open Access | Times Cited: 49
UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases
Wanxue Zhu, Ehsan Eyshi Rezaei, Hamideh Nouri, et al.
Field Crops Research (2022) Vol. 284, pp. 108582-108582
Closed Access | Times Cited: 38
Research hotspots and frontiers in agricultural multispectral technology: Bibliometrics and scientometrics analysis of the Web of Science
Yali Zhang, Dehua Zhao, Hanchao Liu, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 38
Opportunities and challenges in phenotyping row crops using drone‐based RGB imaging
Dorothy D. Sweet, Sara B. Tirado, Nathan M. Springer, et al.
The Plant Phenome Journal (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 34
Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves
Dunliang Wang, Rui Li, Tao Liu, et al.
Field Crops Research (2023) Vol. 304, pp. 109175-109175
Closed Access | Times Cited: 23
Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015–2020 in Shaanxi, China
Hongye Cao, Ling Han, Ming Liu, et al.
Journal of Environmental Sciences (2023) Vol. 149, pp. 358-373
Closed Access | Times Cited: 18
UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning
Wanxue Zhu, Ehsan Eyshi Rezaei, Hamideh Nouri, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 7471-7485
Open Access | Times Cited: 17
A Systematic Review of Vegetation Indices for Potato Growth Monitoring and Tuber Yield Prediction from Remote Sensing
Alex Mukiibi, A. T. B. Machakaire, A.C. Franke, et al.
Potato Research (2024)
Open Access | Times Cited: 7
Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms
Rei Sonobe, Yuhei Hirono, Ayako Oi
Plants (2020) Vol. 9, Iss. 3, pp. 368-368
Open Access | Times Cited: 48
Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation
Bikram Pratap Banerjee, Germán Spangenberg, Surya Kant
Remote Sensing (2020) Vol. 12, Iss. 19, pp. 3164-3164
Open Access | Times Cited: 44
UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax
Mauro Maesano, Sacha Khoury, Farid Nakhle, et al.
Remote Sensing (2020) Vol. 12, Iss. 20, pp. 3464-3464
Open Access | Times Cited: 42
Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
Zhou Tang, Atit Parajuli, Chunpeng James Chen, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 39
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