Yahui Hu

1.8k total citations · 1 hit paper
66 papers, 1.3k citations indexed

About

Yahui Hu is a scholar working on Plant Science, Analytical Chemistry and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yahui Hu has authored 66 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Plant Science, 12 papers in Analytical Chemistry and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yahui Hu's work include Smart Agriculture and AI (26 papers), Leaf Properties and Growth Measurement (12 papers) and Spectroscopy and Chemometric Analyses (12 papers). Yahui Hu is often cited by papers focused on Smart Agriculture and AI (26 papers), Leaf Properties and Growth Measurement (12 papers) and Spectroscopy and Chemometric Analyses (12 papers). Yahui Hu collaborates with scholars based in China, United States and Taiwan. Yahui Hu's co-authors include Guoxiong Zhou, Aibin Chen, Liujun Li, Wenzhuo Zhang, Mingfang He, Weiwei Cai, Jizheng Yi, Mingxuan Li, Guoxiong Zhou and Yanfeng Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Plant Journal.

In The Last Decade

Yahui Hu

60 papers receiving 1.3k citations

Hit Papers

Fast forest fire smoke detection using MVMNet 2022 2026 2023 2024 2022 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yahui Hu China 22 958 366 189 123 116 66 1.3k
Guoxiong Zhou China 23 950 1.0× 399 1.1× 310 1.6× 190 1.5× 205 1.8× 83 1.6k
Huaibo Song China 23 828 0.9× 236 0.6× 326 1.7× 238 1.9× 20 0.2× 65 1.7k
Wenzhuo Zhang China 12 573 0.6× 252 0.7× 78 0.4× 77 0.6× 27 0.2× 21 758
Chengjun Xie China 24 1.6k 1.6× 294 0.8× 430 2.3× 242 2.0× 44 0.4× 86 2.3k
Yuzhen Lu United States 27 1.3k 1.4× 1.2k 3.2× 159 0.8× 331 2.7× 11 0.1× 108 2.5k
Fuzeng Yang China 17 606 0.6× 237 0.6× 191 1.0× 103 0.8× 14 0.1× 50 1.1k
Yongliang Qiao China 22 719 0.8× 179 0.5× 261 1.4× 262 2.1× 7 0.1× 70 1.6k
P. Raja India 15 1.0k 1.1× 415 1.1× 420 2.2× 204 1.7× 11 0.1× 39 1.7k
Filipe Neves dos Santos Portugal 20 815 0.9× 172 0.5× 358 1.9× 188 1.5× 14 0.1× 102 1.4k
Chunjiang Zhao China 22 1.0k 1.1× 251 0.7× 61 0.3× 248 2.0× 7 0.1× 85 1.4k

Countries citing papers authored by Yahui Hu

Since Specialization
Citations

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

Fields of papers citing papers by Yahui Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yahui Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Yahui Hu. A scholar is included among the top collaborators of Yahui Hu 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 Yahui Hu. Yahui Hu 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.
Hu, Yahui, Jiang Chen, Ning‐Ning Zhang, et al.. (2025). Unveiling the significance of AKAP79/150 in the nervous system disorders: An emerging opportunity for future therapies?. Neurobiology of Disease. 206. 106812–106812.
2.
Zhang, Liangji, et al.. (2025). FATDNet: A fusion adversarial network for tomato leaf disease segmentation under complex backgrounds. Computers and Electronics in Agriculture. 234. 110270–110270. 2 indexed citations
3.
Ku, Li‐Jung Elizabeth, et al.. (2025). Healthcare utilization and costs for patients with Parkinson’s disease in Taiwan. BMC Neurology. 25(1). 3–3. 1 indexed citations
4.
Zhou, Guoxiong, Yi Chai, Liujun Li, et al.. (2024). Identification of rice disease under complex background based on PSOC-DRCNet. Expert Systems with Applications. 249. 123643–123643. 21 indexed citations
5.
Zhou, Guoxiong, et al.. (2024). A Precise Framework for Rice Leaf Disease Image–Text Retrieval Using FHTW-Net. Plant Phenomics. 6. 168–168. 11 indexed citations
6.
Zhao, Hongmin, et al.. (2024). A Multi-Modal Open Object Detection Model for Tomato Leaf Diseases with Strong Generalization Performance Using PDC-VLD. Plant Phenomics. 6. 220–220. 13 indexed citations
7.
Hu, Yang, Shuai Liu, Guoxiong Zhou, et al.. (2024). DMFGAN: a multifeature data augmentation method for grape leaf disease identification. The Plant Journal. 120(4). 1278–1303. 2 indexed citations
8.
Huang, Shuangjie, et al.. (2023). Identification of tomato leaf diseases based on multi-channel automatic orientation recurrent attention network. Computers and Electronics in Agriculture. 205. 107605–107605. 49 indexed citations
9.
Wang, Qifan, Weiwei Cai, Yahui Hu, et al.. (2023). Identification of grape leaf diseases based on VN-BWT and Siamese DWOAM-DRNet. Engineering Applications of Artificial Intelligence. 123. 106341–106341. 29 indexed citations
10.
Hu, Yahui, et al.. (2023). Research on Image Analysis and Processing Technology Based on Big Data Technology. 1383–1386. 1 indexed citations
11.
Huang, Chien‐Wei, Kun‐Hua Tu, Kang‐Chih Fan, et al.. (2023). The role of confirmatory tests in the diagnosis of primary aldosteronism. Journal of the Formosan Medical Association. 123. S104–S113. 3 indexed citations
12.
Zhou, Guoxiong, et al.. (2023). A precise apple leaf diseases detection using BCTNet under unconstrained environments. Computers and Electronics in Agriculture. 212. 108132–108132. 33 indexed citations
13.
Tang, Zhiwen, Xinyu He, Guoxiong Zhou, et al.. (2023). A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet. Plant Phenomics. 5. 42–42. 45 indexed citations
14.
Hu, Xiangyun, et al.. (2023). Automatic Detection of Small Sample Apple Surface Defects Using ASDINet. Foods. 12(6). 1352–1352. 6 indexed citations
15.
Li, Mingxuan, Guoxiong Zhou, Aibin Chen, Liujun Li, & Yahui Hu. (2023). Identification of tomato leaf diseases based on LMBRNet. Engineering Applications of Artificial Intelligence. 123. 106195–106195. 51 indexed citations
16.
Li, Zongchen, Guoxiong Zhou, Yaowen Hu, et al.. (2022). Maize leaf disease identification based on WG-MARNet. PLoS ONE. 17(4). e0267650–e0267650. 15 indexed citations
17.
Hu, Yaowen, Guoxiong Zhou, Weiwei Cai, et al.. (2022). DS-MENet for the classification of citrus disease. Frontiers in Plant Science. 13. 884464–884464. 11 indexed citations
18.
Zhou, Guoxiong, et al.. (2020). Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet. IEEE Access. 8. 57952–57966. 152 indexed citations
19.
Hu, Yahui, et al.. (2019). Two new species of Lobellini from Central-South China (Collembola: Neanuridae). Zootaxa. 4712(1). zootaxa.4712.1.5–zootaxa.4712.1.5. 2 indexed citations
20.
Feng, Mingfeng, Hanping Zhang, Yuan Pan, et al.. (2016). Complete nucleotide sequence of strawberry vein banding virus Chinese isolate and infectivity of its full-length DNA clone. Virology Journal. 13(1). 164–164. 16 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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