Jiuxi Li

539 total citations · 1 hit paper
25 papers, 399 citations indexed

About

Jiuxi Li is a scholar working on Plant Science, Automotive Engineering and Control and Systems Engineering. According to data from OpenAlex, Jiuxi Li has authored 25 papers receiving a total of 399 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Plant Science, 6 papers in Automotive Engineering and 6 papers in Control and Systems Engineering. Recurrent topics in Jiuxi Li's work include Smart Agriculture and AI (9 papers), Electric and Hybrid Vehicle Technologies (4 papers) and Vehicle emissions and performance (4 papers). Jiuxi Li is often cited by papers focused on Smart Agriculture and AI (9 papers), Electric and Hybrid Vehicle Technologies (4 papers) and Vehicle emissions and performance (4 papers). Jiuxi Li collaborates with scholars based in China and Mongolia. Jiuxi Li's co-authors include Chun‐Shan Wang, Huarui Wu, Chunjiang Zhao, Huaji Zhu, Pengfei Du, Ji Zhou, Guifa Teng, Qian Wang, Qian Wang and Chunjiang Zhao and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Frontiers in Plant Science.

In The Last Decade

Jiuxi Li

20 papers receiving 388 citations

Hit Papers

A cucumber leaf disease severity classification method ba... 2021 2026 2022 2024 2021 50 100 150
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. A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net
    2021 174 citations Chun‐Shan Wang, Pengfei Du 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
Jiuxi Li China 9 304 87 53 32 30 25 399
Guofeng Yang China 11 247 0.8× 114 1.3× 59 1.1× 19 0.6× 24 0.8× 22 354
Yanlei Xu China 10 264 0.9× 84 1.0× 68 1.3× 15 0.5× 39 1.3× 28 353
Zhenzhen Song China 6 229 0.8× 63 0.7× 40 0.8× 22 0.7× 35 1.2× 13 312
Huaji Zhu China 9 243 0.8× 71 0.8× 38 0.7× 29 0.9× 16 0.5× 30 399
S. K. Bodhe India 10 337 1.1× 192 2.2× 51 1.0× 21 0.7× 25 0.8× 34 489
Yanfeng Wang China 13 240 0.8× 92 1.1× 25 0.5× 24 0.8× 67 2.2× 26 425
Yali Feng China 7 338 1.1× 107 1.2× 50 0.9× 19 0.6× 55 1.8× 12 482
José Delpiano Chile 10 168 0.6× 51 0.6× 47 0.9× 16 0.5× 45 1.5× 28 419
Yuyu Tian China 4 239 0.8× 82 0.9× 42 0.8× 13 0.4× 37 1.2× 7 314
Yun Peng China 9 301 1.0× 82 0.9× 38 0.7× 14 0.4× 25 0.8× 13 379

Countries citing papers authored by Jiuxi Li

Since Specialization
Citations

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

Fields of papers citing papers by Jiuxi Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiuxi Li

This figure shows the co-authorship network connecting the top 25 collaborators of Jiuxi Li. A scholar is included among the top collaborators of Jiuxi Li 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 Jiuxi Li. Jiuxi Li 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.
Wang, Xuejing, Huan Cui, Jue Wang, et al.. (2025). Distribution of Aerosol Bacteria in Broiler Houses at Different Growth Stages During Winter. Animals. 15(19). 2859–2859. 2 indexed citations
2.
Wang, Chun‐Shan, et al.. (2023). A Cucumber Leaf Disease Severity Grading Method in Natural Environment Based on the Fusion of TRNet and U-Net. Agronomy. 14(1). 72–72. 10 indexed citations
3.
Sun, Wei, et al.. (2023). Veg-DenseCap: Dense Captioning Model for Vegetable Leaf Disease Images. Agronomy. 13(7). 1700–1700. 7 indexed citations
4.
Sun, Wei, et al.. (2023). DFYOLOv5m-M2transformer: Interpretation of vegetable disease recognition results using image dense captioning techniques. Computers and Electronics in Agriculture. 215. 108460–108460. 5 indexed citations
5.
Wang, Chun‐Shan, Ji Zhou, Yan Zhang, et al.. (2022). A Plant Disease Recognition Method Based on Fusion of Images and Graph Structure Text. Frontiers in Plant Science. 12. 731688–731688. 12 indexed citations
6.
Wang, Chun‐Shan, Ji Zhou, Chunjiang Zhao, et al.. (2021). Few-shot vegetable disease recognition model based on image text collaborative representation learning. Computers and Electronics in Agriculture. 184. 106098–106098. 37 indexed citations
7.
Wang, Chun‐Shan, Pengfei Du, Huarui Wu, et al.. (2021). A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net. Computers and Electronics in Agriculture. 189. 106373–106373. 174 indexed citations breakdown →
8.
Zhou, Ji, Jiuxi Li, Chun‐Shan Wang, et al.. (2021). Crop disease identification and interpretation method based on multimodal deep learning. Computers and Electronics in Agriculture. 189. 106408–106408. 67 indexed citations
9.
Wang, Chun‐Shan, Qian Wang, Huarui Wu, et al.. (2021). Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3. Remote Sensing. 13(11). 2130–2130. 19 indexed citations
10.
Wang, Chun‐Shan, Ji Zhou, Huarui Wu, et al.. (2020). Research on the Evaluation Method of Eggshell Dark Spots Based on Machine Vision. IEEE Access. 8. 160116–160125. 8 indexed citations
11.
Wang, Chun‐Shan, et al.. (2020). Identification of Growing Points of Cotton Main Stem Based on Convolutional Neural Network. IEEE Access. 8. 208407–208417. 8 indexed citations
12.
Tian, Miao, et al.. (2014). HEV Test Bench Based on CAN Bus Sensor Communication. SHILAP Revista de lepidopterología. 164(2). 163–169. 1 indexed citations
13.
Zhang, Shifang, et al.. (2013). Study of Fuel Consumption Test Method for Hybrid Vehicles. Journal of Convergence Information Technology. 8(8). 759–767.
14.
Tian, Miao, et al.. (2013). Control Information and Data Processing of Vehicle based on Global Position System. Journal of Networks. 8(5).
15.
Zhang, Shifang, et al.. (2013). Data Process of Diagnose Expert System based on Neural Network. Journal of Networks. 8(12). 1 indexed citations
16.
Li, Lihua, et al.. (2012). Design and experiment on monitoring device for layers individual production performance parameters. Nongye gongcheng xuebao. 2012(4). 4 indexed citations
17.
Li, Jiuxi, et al.. (2009). Intelligent Speed Adaptation Impact of Fuel Consumption and Emission. 1311–1316. 2 indexed citations
18.
Zhang, Shifang, et al.. (2007). Hybrid electric city bus Driving cycle Testing and Analysis Based on Data Stream. 5. 1–11. 3 indexed citations
19.
Wang, Chun‐Shan, Jiuxi Li, Guifa Teng, et al.. (2007). Study on Intelligent Speed Adaptation Impact of Driving Safety Based on Simulation. 24. 537–537.
20.
Wang, Chun‐Shan, et al.. (2007). Intelligent Speed Adaptation Impact of Driving Safety. 31. 3654–3659. 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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