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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:

Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat
Zhaopeng Fu, Shanshan Yu, Jiayi Zhang, et al.
European Journal of Agronomy (2021) Vol. 132, pp. 126405-126405
Closed Access | Times Cited: 63

Showing 1-25 of 63 citing articles:

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery
Yulin Shen, Benoît Mercatoris, Zhen Cao, et al.
Agriculture (2022) Vol. 12, Iss. 6, pp. 892-892
Open Access | Times Cited: 44
High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing
Huichun Zhang, Lu Wang, Xiuliang Jin, et al.
The Crop Journal (2023) Vol. 11, Iss. 5, pp. 1303-1318
Open Access | Times Cited: 39
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: 37
Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing
Qiang Wu, Yongping Zhang, Zhiwei Zhao, et al.
Agronomy (2023) Vol. 13, Iss. 1, pp. 211-211
Open Access | Times Cited: 36
Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data
Md. Suruj Mia, Ryoya Tanabe, Luthfan Nur Habibi, et al.
Remote Sensing (2023) Vol. 15, Iss. 10, pp. 2511-2511
Open Access | Times Cited: 30
Global Research Trends for Unmanned Aerial Vehicle Remote Sensing Application in Wheat Crop Monitoring
Lwandile Nduku, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, et al.
Geomatics (2023) Vol. 3, Iss. 1, pp. 115-136
Open Access | Times Cited: 28
A Machine-Learning Model Based on the Fusion of Spectral and Textural Features from UAV Multi-Sensors to Analyse the Total Nitrogen Content in Winter Wheat
Zongpeng Li, Xinguo Zhou, Qian Cheng, et al.
Remote Sensing (2023) Vol. 15, Iss. 8, pp. 2152-2152
Open Access | Times Cited: 27
A spectral decomposition method for estimating the leaf nitrogen status of maize by UAV-based hyperspectral imaging
Meiyan Shu, Zhu Jinyu, Yang XiaoHong, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108100-108100
Closed Access | Times Cited: 24
A novel framework to assess apple leaf nitrogen content: Fusion of hyperspectral reflectance and phenology information through deep learning
Riqiang Chen, Wenping Liu, Hao Yang, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108816-108816
Closed Access | Times Cited: 10
Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis
Shaohua Zhang, Jianzhao Duan, Xinghui Qi, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109022-109022
Closed Access | Times Cited: 10
Enhancing in-season yield forecast accuracy for film-mulched wheat: A hybrid approach coupling crop model and UAV remote-sensing data by ensemble learning technique
Zhikai Cheng, Xiaobo Gu, Zhihui Zhou, et al.
European Journal of Agronomy (2024) Vol. 156, pp. 127174-127174
Closed Access | Times Cited: 8
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: 37
Monitoring Key Wheat Growth Variables by Integrating Phenology and UAV Multispectral Imagery Data into Random Forest Model
Shaoyu Han, Yu Zhao, Jinpeng Cheng, et al.
Remote Sensing (2022) Vol. 14, Iss. 15, pp. 3723-3723
Open Access | Times Cited: 34
Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion
Hansu Zhang, Linsheng Huang, Wenjiang Huang, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 31
Drone remote sensing of wheat N using hyperspectral sensor and machine learning
Rabi Narayan Sahoo, R. G. Rejith, Shalini Gakhar, et al.
Precision Agriculture (2023) Vol. 25, Iss. 2, pp. 704-728
Closed Access | Times Cited: 17
Monitoring leaf area index of the sown mixture pasture through UAV multispectral image and texture characteristics
Xiaoxue Wang, Shicheng Yan, Wenting Wang, et al.
Computers and Electronics in Agriculture (2023) Vol. 214, pp. 108333-108333
Closed Access | Times Cited: 16
Using the time series nitrogen diagnosis curve for precise nitrogen management in wheat and rice
Zhaopeng Fu, Jiayi Zhang, Jie Jiang, et al.
Field Crops Research (2024) Vol. 307, pp. 109259-109259
Closed Access | Times Cited: 7
Multi-source data fusion improved the potential of proximal fluorescence sensors in predicting nitrogen nutrition status across winter wheat growth stages
Qing Liu, Cuicun Wang, Jie Jiang, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108786-108786
Closed Access | Times Cited: 6
Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season
Liyuan Zhang, Xiaoying Song, Yaxiao Niu, et al.
Agriculture (2024) Vol. 14, Iss. 3, pp. 456-456
Open Access | Times Cited: 6
Estimation of sugar content in sugar beet root based on UAV multi-sensor data
Qing Wang, Yingpu Che, Ke Shao, et al.
Computers and Electronics in Agriculture (2022) Vol. 203, pp. 107433-107433
Closed Access | Times Cited: 25
Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize
Zhikai Cheng, Xiaobo Gu, Zhihui Zhou, et al.
Computers and Electronics in Agriculture (2024) Vol. 224, pp. 109190-109190
Closed Access | Times Cited: 5
Estimating Leaf Chlorophyll Content of Winter Wheat from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions
Liyuan Zhang, Aichen Wang, Huiyue Zhang, et al.
Agriculture (2024) Vol. 14, Iss. 7, pp. 1064-1064
Open Access | Times Cited: 5
A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species
Diogo Castilho, Danilo Tedesco, Carlos Eduardo Hernández, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 4
Assessing Data and Sample Complexity in Unmanned Aerial Vehicle Imagery for Agricultural Pattern Classification
Linara Arslanova, Sören Hese, Marcel Fölsch, et al.
Smart Agricultural Technology (2025), pp. 100799-100799
Open Access
Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics
Donghui Zhang, Liang Hou, Liangjie Lv, et al.
Agriculture (2025) Vol. 15, Iss. 3, pp. 326-326
Open Access
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