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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:

Diagnosis of nitrogen status in winter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images
Shishi Liu, Lantao Li, Wenhan Gao, et al.
Computers and Electronics in Agriculture (2018) Vol. 151, pp. 185-195
Closed Access | Times Cited: 79

Showing 1-25 of 79 citing articles:

UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras
Lei Deng, Zhihui Mao, Xiaojuan Li, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2018) Vol. 146, pp. 124-136
Closed Access | Times Cited: 388
A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses
Jayme Garcia Arnal Barbedo
Drones (2019) Vol. 3, Iss. 2, pp. 40-40
Open Access | Times Cited: 309
Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images
Yue Zhang, Chenzhen Xia, Xingyu Zhang, et al.
Ecological Indicators (2021) Vol. 129, pp. 107985-107985
Open Access | Times Cited: 124
Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture
Daniel Olson, James V. Anderson
Agronomy Journal (2021) Vol. 113, Iss. 2, pp. 971-992
Open Access | Times Cited: 106
Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images
Yinuo Liu, Shishi Liu, Jing Li, et al.
Computers and Electronics in Agriculture (2019) Vol. 166, pp. 105026-105026
Closed Access | Times Cited: 117
Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes - A systematic review
Felipe Librán‐Embid, Felix Klaus, Teja Tscharntke, et al.
The Science of The Total Environment (2020) Vol. 732, pp. 139204-139204
Closed Access | Times Cited: 96
Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression
Yuanyuan Fu, Guijun Yang, Zhenhai Li, et al.
Remote Sensing (2020) Vol. 12, Iss. 22, pp. 3778-3778
Open Access | Times Cited: 74
Random forest regression results in accurate assessment of potato nitrogen status based on multispectral data from different platforms and the critical concentration approach
Junxiang Peng, Kiril Manevski, Kirsten Kørup, et al.
Field Crops Research (2021) Vol. 268, pp. 108158-108158
Closed Access | Times Cited: 61
The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines
Meiyan Shu, Mengyuan Shen, Jinyu Zuo, et al.
Plant Phenomics (2021) Vol. 2021
Open Access | Times Cited: 59
Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass
Tianyue Xu, Fumin Wang, Lili Xie, et al.
Remote Sensing (2022) Vol. 14, Iss. 11, pp. 2534-2534
Open Access | Times Cited: 49
Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics
Mohamed Farag Taha, Alwaseela Abdalla, Gamal ElMasry, et al.
Chemosensors (2022) Vol. 10, Iss. 2, pp. 45-45
Open Access | Times Cited: 46
Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages
Tianyue Xu, Fumin Wang, Zhou Shi, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 202, pp. 169-183
Closed Access | Times Cited: 24
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: 13
Quantifying corn LAI using machine learning and UAV multispectral imaging
Qian Cheng, Fan Ding, Xu HongGang, et al.
Precision Agriculture (2024) Vol. 25, Iss. 4, pp. 1777-1799
Closed Access | Times Cited: 9
Characterization of N variations in different organs of winter wheat and mapping NUE using low altitude UAV-based remote sensing
Falv Wang, Jingcheng Zhang, Wei Li, et al.
Precision Agriculture (2025) Vol. 26, Iss. 2
Open Access | Times Cited: 1
Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-Enabled Dynamic Model
Alwaseela Abdalla, Haiyan Cen, Liang Wan, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 6, pp. 4379-4389
Closed Access | Times Cited: 59
Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models
Dragana Rajković, Ana Marjanović‐Jeromela, Lato Pezo, et al.
Agronomy (2021) Vol. 12, Iss. 1, pp. 58-58
Open Access | Times Cited: 51
Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images
Shengzhao Pei, Hualiang Zeng, Yulong Dai, et al.
Journal of Integrative Agriculture (2023) Vol. 22, Iss. 8, pp. 2536-2552
Open Access | Times Cited: 21
Using an optimized texture index to monitor the nitrogen content of potato plants over multiple growth stages
Yiguang Fan, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108147-108147
Closed Access | Times Cited: 21
Assessing maize lodging severity using multitemporal UAV-based digital images
Meiyan Shu, Bai Kai, Lei Meng, et al.
European Journal of Agronomy (2023) Vol. 144, pp. 126754-126754
Closed Access | Times Cited: 20
A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery
Zhenqi Liao, Yulong Dai, Han Wang, et al.
Journal of Integrative Agriculture (2023) Vol. 22, Iss. 7, pp. 2248-2270
Open Access | Times Cited: 19
Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency
Jingcheng Zhang, Yuncai Hu, Fei Li, et al.
Remote Sensing (2024) Vol. 16, Iss. 5, pp. 838-838
Open Access | Times Cited: 8
Multi-scale monitoring of rice aboveground biomass by combining spectral and textural information from UAV hyperspectral images
Tianyue Xu, Fumin Wang, Zhou Shi, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 127, pp. 103655-103655
Open Access | Times Cited: 7
Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data
Tingting Chen, Weiguang Yang, Huajian Zhang, et al.
Computers and Electronics in Agriculture (2020) Vol. 177, pp. 105708-105708
Closed Access | Times Cited: 49
Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring
Jian Zhang, Chufeng Wang, Chenghai Yang, et al.
Remote Sensing (2020) Vol. 12, Iss. 7, pp. 1207-1207
Open Access | Times Cited: 43
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