Caicong Wu
Impact in
- Food Science top 10%
- Food Supply Chain Traceability
-
- Smart Agriculture and AI
Papers in
-
- Smart Agriculture and AI 17
- Food Science 11
- Food Supply Chain Traceability 11
- Co-authors
- Guangyuan Li (3 shared papers)Xiaoqiang Zhang (4 shared papers)Kun Zhou (5 shared papers)Weixin Zhai (12 shared papers)Ying Chen (1 shared paper)Xiaoqiang Zhang (1 shared paper)Lili Yang (6 shared papers)Jiawen Pan (11 shared papers)
- Journals
- Computers and Electronics in Agriculture (17 papers)International journal of agricultural and biological engineering (5 papers)Energies (1 paper)Mathematical and Computer Modelling (1 paper)Agronomy (1 paper)
- Partner nations
- ChinaUnited StatesDenmark
In The Last Decade
Caicong Wu
44 papers receiving 319 citations
Peers
Comparison fields: 5 of 65
- Food Science 79
- Plant Science 142
- Computer Vision and Pattern Recognition 72
- Environmental Engineering 36
- Industrial and Manufacturing Engineering 25
Countries citing papers authored by Caicong Wu
This map shows the geographic impact of Caicong 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 Caicong Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caicong Wu more than expected).
Fields of papers citing papers by Caicong Wu
This network shows the impact of papers produced by Caicong 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 Caicong Wu. The network helps show where Caicong Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Caicong 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 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 35 | |
| 2 | 2010 | 30 | |
| 3 | 2022 | 30 | |
| 4 | 2023 | 23 | |
| 5 | 2023 | 23 | |
| 6 | 2022 | 19 | |
| 7 | 2023 | 13 | |
| 8 | 2023 | 12 | |
| 9 | 2024 | 12 | |
| 10 | 2023 | 12 | |
| 11 | 2016 | 11 | |
| 12 | 2020 | 10 | |
| 13 | 2023 | 10 | |
| 14 | 2023 | 8 | |
| 15 | 2024 | 8 | |
| 16 | 2024 | 8 | |
| 17 | 2022 | 8 | |
| 18 | 2018 | 6 | |
| 19 | 2023 | 5 | |
| 20 | 2018 | 5 |
About Caicong Wu
Caicong Wu is a scholar working on Plant Science, Food Science, Computer Vision and Pattern Recognition, Civil and Structural Engineering and Control and Systems Engineering, having authored 48 papers that have together received 334 indexed citations. Recurring topics across this work include Smart Agriculture and AI (17 papers), Food Supply Chain Traceability (11 papers), Remote Sensing and LiDAR Applications (5 papers), Soil Mechanics and Vehicle Dynamics (5 papers), Agricultural Engineering and Mechanization (5 papers), Image Processing and 3D Reconstruction (4 papers), Advanced Measurement and Detection Methods (3 papers) and Data Management and Algorithms (2 papers). The work is most often cited by research in Food Science (79 citations), Plant Science (142 citations), Computer Vision and Pattern Recognition (72 citations), Environmental Engineering (36 citations) and Industrial and Manufacturing Engineering (25 citations). Caicong Wu has collaborated with scholars based in China, United States and Denmark. Frequent co-authors include Guangyuan Li, Xiaoqiang Zhang, Kun Zhou, Weixin Zhai, Ying Chen, Xiaoqiang Zhang, Lili Yang, Jiawen Pan, Yubin Lan and Ying Chen. Their work appears in journals such as Computers and Electronics in Agriculture, International journal of agricultural and biological engineering, Energies, Mathematical and Computer Modelling and Agronomy.
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.