Ce Yang

1.9k total citations · 1 hit paper
40 papers, 1.4k citations indexed

About

Ce Yang is a scholar working on Plant Science, Analytical Chemistry and Ecology. According to data from OpenAlex, Ce Yang has authored 40 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Plant Science, 27 papers in Analytical Chemistry and 26 papers in Ecology. Recurrent topics in Ce Yang's work include Spectroscopy and Chemometric Analyses (27 papers), Remote Sensing in Agriculture (25 papers) and Smart Agriculture and AI (18 papers). Ce Yang is often cited by papers focused on Spectroscopy and Chemometric Analyses (27 papers), Remote Sensing in Agriculture (25 papers) and Smart Agriculture and AI (18 papers). Ce Yang collaborates with scholars based in United States, China and Iran. Ce Yang's co-authors include Chuanqi Xie, Yong He, Won Suk Lee, Brian J. Steffenson, Cory D. Hirsch, Wei Yang, Ziyuan Hao, Paul Gader, Ali Moghimi and Wen‐Hao Su and has published in prestigious journals such as The Science of The Total Environment, Scientific Reports and Food Chemistry.

In The Last Decade

Ce Yang

36 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A review on plant high-throughput phenotyping traits using UAV-based sensors

2020 251 citations Ce Yang et al. Computers and Electronics in Agriculture profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ce Yang United States	19	987	579	573	177	106	40	1.4k
Huiqin Ma China	22	979 1.0×	585 1.0×	714 1.2×	173 1.0×	69 0.7×	44	1.5k
Till Rumpf Germany	6	1.2k 1.2×	748 1.3×	717 1.3×	108 0.6×	58 0.5×	7	1.5k
C. Bravo Belgium	11	992 1.0×	788 1.4×	632 1.1×	84 0.5×	105 1.0×	14	1.4k
Gensheng Hu China	18	947 1.0×	412 0.7×	291 0.5×	134 0.8×	72 0.7×	53	1.3k
Haiyong Weng China	16	779 0.8×	366 0.6×	491 0.9×	199 1.1×	96 0.9×	43	1.2k
Mirwaes Wahabzada Germany	18	774 0.8×	497 0.9×	548 1.0×	85 0.5×	45 0.4×	22	1.2k
G. Polder Netherlands	23	1.5k 1.6×	952 1.6×	595 1.0×	135 0.8×	237 2.2×	75	2.3k
Juntao Xiong China	21	1.2k 1.2×	410 0.7×	188 0.3×	132 0.7×	181 1.7×	49	1.6k
Huanyu Jiang China	19	1.1k 1.1×	360 0.6×	261 0.5×	152 0.9×	117 1.1×	85	1.5k

Countries citing papers authored by Ce Yang

Since Specialization

Citations

This map shows the geographic impact of Ce Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ce Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ce Yang more than expected).

Fields of papers citing papers by Ce Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ce Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ce Yang. The network helps show where Ce Yang may publish in the future.

Co-authorship network of co-authors of Ce Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Ce Yang. A scholar is included among the top collaborators of Ce Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ce Yang. Ce Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Sanaeifar, Alireza, et al.. (2025). High-Throughput UAV Hyperspectral Remote Sensing Pinpoints Bacterial Leaf Streak Resistance in Wheat. Remote Sensing. 17(16). 2799–2799.

2.

Sanaeifar, Alireza, Shahryar F. Kianian, Ruth Dill‐Macky, et al.. (2025). Transformer-based and band-selected models for UAV hyperspectral wheat disease classification. Smart Agricultural Technology. 13. 101714–101714.

3.

Liu, Yarui, et al.. (2025). Winter Wheat Canopy Height Estimation Based on the Fusion of LiDAR and Multispectral Data. Agronomy. 15(5). 1094–1094.

4.

Li, Xuechen, Lang Qiao, & Ce Yang. (2025). AgriFusion: Multiscale RGB–NIR Fusion for Semantic Segmentation in Airborne Agricultural Imagery. AgriEngineering. 7(11). 388–388.

5.

Sanaeifar, Alireza, Ce Yang, Min An, et al.. (2024). Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging. Remote Sensing. 16(1). 187–187. 11 indexed citations

6.

Sanaeifar, Alireza, et al.. (2023). Advancing precision agriculture: The potential of deep learning for cereal plant head detection. Computers and Electronics in Agriculture. 209. 107875–107875. 45 indexed citations

7.

Li, Xue, et al.. (2023). Monitoring Indicators for Comprehensive Growth of Summer Maize Based on UAV Remote Sensing. Agronomy. 13(12). 2888–2888. 8 indexed citations

8.

Abdulridha, Jaafar, Min An, Matthew N. Rouse, et al.. (2023). Evaluation of Stem Rust Disease in Wheat Fields by Drone Hyperspectral Imaging. Sensors. 23(8). 4154–4154. 11 indexed citations

9.

Wu, Jianshuang, et al.. (2022). DS-DETR: A Model for Tomato Leaf Disease Segmentation and Damage Evaluation. Agronomy. 12(9). 2023–2023. 29 indexed citations

10.

Li, Xiuhua, et al.. (2022). Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery. Sensors. 22(7). 2711–2711. 25 indexed citations

11.

Zhang, Jiajing, Min An, Brian J. Steffenson, et al.. (2022). Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model. Frontiers in Plant Science. 13. 834938–834938. 13 indexed citations

12.

Sanaeifar, Alireza, Ce Yang, Miguel de la Guárdia, et al.. (2022). Proximal hyperspectral sensing of abiotic stresses in plants. The Science of The Total Environment. 861. 160652–160652. 56 indexed citations

13.

Nigon, Tyler J., et al.. (2021). The Influence of Aerial Hyperspectral Image Processing Workflow on Nitrogen Uptake Prediction Accuracy in Maize. Remote Sensing. 14(1). 132–132. 4 indexed citations

14.

Nigon, Tyler J., et al.. (2020). Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery. Remote Sensing. 12(8). 1234–1234. 31 indexed citations

15.

Su, Wen‐Hao, et al.. (2020). Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sensing. 13(1). 26–26. 102 indexed citations

16.

Minaei, Saeid, et al.. (2020). Acoustic features of vocalization signal in poultry health monitoring. Applied Acoustics. 175. 107756–107756. 38 indexed citations

17.

Su, Wen‐Hao, et al.. (2020). Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening. Food Chemistry. 343. 128507–128507. 62 indexed citations

18.

Qiu, Ruicheng, et al.. (2019). Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sensing. 11(22). 2658–2658. 77 indexed citations

19.

Yang, Ce, et al.. (2018). Spectral reflectance response to nitrogen fertilization in field grown corn. International journal of agricultural and biological engineering. 11(4). 118–126. 4 indexed citations

20.

Yang, Ce, et al.. (2018). A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging. Frontiers in Plant Science. 9. 1182–1182. 56 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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