Xiaoping Wu
Impact in
- Plant Science top 10%
- Smart Agriculture and AI
- Date Palm Research Studies
Papers in
-
- Multimodal Machine Learning Applications 3
- Advanced Image and Video Retrieval Techniques 3
- Face recognition and analysis 2
-
- Domain Adaptation and Few-Shot Learning 4
- Co-authors
- Jufeng Yang (5 shared papers)Ming‐Ming Cheng (3 shared papers)Yu‐Kun Lai (3 shared papers)Jie Liang (2 shared papers)Liang Wang (1 shared paper)Paul L. Rosin (1 shared paper)Xiaoxiao Sun (1 shared paper)Lufeng Mo (5 shared papers)
- Journals
- Drones (2 papers)Forests (1 paper)Journal of Food Composition and Analysis (1 paper)RSC Advances (1 paper)Electronics (1 paper)
- Partner nations
- ChinaUnited KingdomChile
In The Last Decade
Xiaoping Wu
20 papers receiving 581 citations
Xiaoping Wu's Hit Papers
Peers
Comparison fields: 5 of 101
- Plant Science 272
- Computer Vision and Pattern Recognition 114
- Analytical Chemistry 48
- Ecological Modeling 21
- Dermatology 41
Countries citing papers authored by Xiaoping Wu
This map shows the geographic impact of Xiaoping Wu'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 Xiaoping Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoping Wu more than expected).
Fields of papers citing papers by Xiaoping Wu
This network shows the impact of papers produced by Xiaoping Wu. 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 Xiaoping Wu. The network helps show where Xiaoping Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Xiaoping Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition Hit paper breakdown → | 2019 | 311 |
| 2 | 2019 | 72 | |
| 3 | 2019 | 58 | |
| 4 | 2020 | 21 | |
| 5 | 2022 | 17 | |
| 6 | 2020 | 17 | |
| 7 | 2007 | 17 | |
| 8 | 2022 | 16 | |
| 9 | 2023 | 15 | |
| 10 | 2020 | 14 | |
| 11 | 2016 | 9 | |
| 12 | 2020 | 9 | |
| 13 | 2021 | 8 | |
| 14 | 2023 | 7 | |
| 15 | 2023 | 5 | |
| 16 | 2023 | 4 | |
| 17 | 2022 | 2 | |
| 18 | 2023 | 2 | |
| 19 | 2018 | 1 | |
| 20 | 1994 | 1 |
About Xiaoping Wu
Xiaoping Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Plant Science, Biomedical Engineering and Molecular Biology, having authored 22 papers that have together received 606 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Smart Agriculture and AI (4 papers), Advanced Chemical Sensor Technologies (3 papers), Multimodal Machine Learning Applications (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Spectroscopy and Chemometric Analyses (2 papers), Face recognition and analysis (2 papers) and Remote Sensing and LiDAR Applications (2 papers). The work is most often cited by research in Plant Science (272 citations), Computer Vision and Pattern Recognition (114 citations), Analytical Chemistry (48 citations), Ecological Modeling (21 citations) and Dermatology (41 citations). Xiaoping Wu has collaborated with scholars based in China, United Kingdom and Chile. Frequent co-authors include Jufeng Yang, Ming‐Ming Cheng, Yu‐Kun Lai, Jie Liang, Liang Wang, Paul L. Rosin, Xiaoxiao Sun, Lufeng Mo, Xiaomei Yi and Wen Ni. Their work appears in journals such as Drones, Forests, Journal of Food Composition and Analysis, RSC Advances and Electronics.
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.