Xi Cheng

1.2k total citations · 1 hit paper
48 papers, 799 citations indexed

About

Xi Cheng is a scholar working on Plant Science, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Xi Cheng has authored 48 papers receiving a total of 799 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Plant Science, 15 papers in Computer Vision and Pattern Recognition and 9 papers in Media Technology. Recurrent topics in Xi Cheng's work include Advanced Image Processing Techniques (11 papers), Nematode management and characterization studies (10 papers) and Image and Signal Denoising Methods (7 papers). Xi Cheng is often cited by papers focused on Advanced Image Processing Techniques (11 papers), Nematode management and characterization studies (10 papers) and Image and Signal Denoising Methods (7 papers). Xi Cheng collaborates with scholars based in China, United States and Hong Kong. Xi Cheng's co-authors include Youhua Zhang, Yunzhi Wu, Yi Yue, Yiqiong Chen, Qiang Dai, Jian Yang, Yan Qiao, Dongliang Chen, Xiang Li and Ying Tai and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Xi Cheng

48 papers receiving 763 citations

Hit Papers

Pest identification via deep residual learning in complex... 2017 2026 2020 2023 2017 50 100 150 200 250
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.

  1. Pest identification via deep residual learning in complex background
    2017 273 citations Xi Cheng, Youhua Zhang 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 Career Trend Papers Cites
Xi Cheng China 14 535 143 138 93 89 48 799
Amy Tabb United States 15 565 1.1× 117 0.8× 151 1.1× 148 1.6× 133 1.5× 31 865
Chunlei Xia China 17 365 0.7× 71 0.5× 152 1.1× 73 0.8× 148 1.7× 47 860
Lin Jiao China 16 475 0.9× 81 0.6× 84 0.6× 111 1.2× 97 1.1× 46 910
Bo Jiang China 19 298 0.6× 167 1.2× 159 1.2× 34 0.4× 111 1.2× 127 1.0k
Yunzhi Wu China 8 401 0.7× 70 0.5× 61 0.4× 83 0.9× 69 0.8× 15 533
Limiao Deng China 15 461 0.9× 68 0.5× 98 0.7× 320 3.4× 65 0.7× 36 808
Yi Yue China 11 458 0.9× 304 2.1× 46 0.3× 66 0.7× 87 1.0× 19 793
Nisar Ahmed Pakistan 17 675 1.3× 116 0.8× 107 0.8× 49 0.5× 36 0.4× 74 1.1k
Gercina Gonçalves da Silva Brazil 6 572 1.1× 49 0.3× 49 0.4× 168 1.8× 220 2.5× 9 720
Mads Dyrmann Denmark 15 969 1.8× 64 0.4× 68 0.5× 254 2.7× 422 4.7× 32 1.2k

Countries citing papers authored by Xi Cheng

Since Specialization
Citations

This map shows the geographic impact of Xi Cheng'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 Xi Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xi Cheng more than expected).

Fields of papers citing papers by Xi Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xi Cheng. 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 Xi Cheng. The network helps show where Xi Cheng may publish in the future.

Co-authorship network of co-authors of Xi Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Xi Cheng. A scholar is included among the top collaborators of Xi Cheng 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 Xi Cheng. Xi Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dai, Qiang, et al.. (2023). Adaptive Multispectral Encoding Network for Image Demoiréing. IEEE Transactions on Instrumentation and Measurement. 72. 1–14. 4 indexed citations
2.
Dai, Qiang, Xi Cheng, & Li Zhang. (2023). Image denoising using channel attention residual enhanced Swin Transformer. Multimedia Tools and Applications. 83(7). 19041–19059. 2 indexed citations
3.
Gao, Kang, et al.. (2023). Research advance on cold tolerance in chrysanthemum. Frontiers in Plant Science. 14. 1259229–1259229. 2 indexed citations
4.
Wang, Hongli, et al.. (2023). Cold tolerance identification of nine Rosa L. materials and expression patterns of genes related to cold tolerance in Rosa hybrida. Frontiers in Plant Science. 14. 1209134–1209134. 4 indexed citations
5.
Ding, Jie, Cheng Zhang, Xi Cheng, et al.. (2023). Method for Classifying Apple Leaf Diseases Based on Dual Attention and Multi-Scale Feature Extraction. Agriculture. 13(5). 940–940. 9 indexed citations
6.
Cheng, Xi, et al.. (2022). Fast and Lightweight Network for Single Frame Structured Illumination Microscopy Super-Resolution. IEEE Transactions on Instrumentation and Measurement. 71. 1–11. 16 indexed citations
7.
Liu, Yue, et al.. (2022). Transcriptomic analysis of ncRNA and mRNA interactions during leaf senescence in tomato. International Journal of Biological Macromolecules. 222(Pt B). 2556–2570. 10 indexed citations
8.
9.
Cheng, Xi, Zhenyong Fu, & Jian Yang. (2021). Improved multi-scale dynamic feature encoding network for image demoiréing. Pattern Recognition. 116. 107970–107970. 14 indexed citations
10.
Dai, Qiang, Xi Cheng, Yan Qiao, & Youhua Zhang. (2020). Crop Leaf Disease Image Super-Resolution and Identification With Dual Attention and Topology Fusion Generative Adversarial Network. IEEE Access. 8. 55724–55735. 48 indexed citations
11.
Dai, Qiang, Xi Cheng, Yan Qiao, & Youhua Zhang. (2020). Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network. IEEE Access. 8. 81943–81959. 28 indexed citations
12.
Zhang, Fugui, Xin Xiao, Kun Xu, et al.. (2020). Genome-wide association study (GWAS) reveals genetic loci of lead (Pb) tolerance during seedling establishment in rapeseed (Brassica napus L.). BMC Genomics. 21(1). 139–139. 16 indexed citations
13.
Cheng, Xi, Qinqin Wu, Yinghui Guo, et al.. (2019). A genome-wide analysis of the cellulose synthase-like (Csl) gene family in maize. Biologia Plantarum. 63. 721–732. 8 indexed citations
14.
15.
Xiao, Shun, et al.. (2019). Description of Labrys fuzhouensis sp. n. and first record of Coslenchus rafiqi (Nematoda: Tylenchidae) from China. Nematology. 21(7). 693–708. 2 indexed citations
16.
17.
Yue, Yi, Xi Cheng, Yunzhi Wu, et al.. (2018). Deep recursive super resolution network with Laplacian Pyramid for better agricultural pest surveillance and detection. Computers and Electronics in Agriculture. 150. 26–32. 45 indexed citations
18.
Xiao, Shun, et al.. (2017). First Report of Meloidogyne enterolobii on Ginger (Zingiber officinale) in China. Plant Disease. 102(3). 684–684. 5 indexed citations
19.
20.
Cheng, Xi. (2009). Application of Wavelet De-noising Theory in Logging Signal Processing. Well Logging Technology. 1 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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