Lie Tang
- Plant Science top 2%
- Smart Agriculture and AI 48
- Leaf Properties and Growth Measurement 20
- Greenhouse Technology and Climate Control 10
- Environmental Engineering top 2%
- Remote Sensing and LiDAR Applications 23
- Analytical Chemistry top 2%
- Spectroscopy and Chemometric Analyses 11
- Automotive Engineering top 5%
- Ecology top 5%
- Remote Sensing in Agriculture 22
-
- Soil Mechanics and Vehicle Dynamics 16
-
- Iterative Learning Control Systems 7
- Co-authors
- Robert G. LandersBrian L. StewardJingyao GaiYin BaoJian JinPatrick S. SchnableLirong XiangMaria G. Salas Fernandez
- Journals
- Computers and Electronics in Agriculture (12 papers)Journal of Manufacturing Science and Engineering (7 papers)Transactions of the ASABE (7 papers)
- Partner nations
- United StatesChinaNetherlands
In The Last Decade
Lie Tang
107 papers receiving 2.5k citations
Peers
Comparison fields: 5 of 118
- Plant Science 1.3k
- Environmental Engineering 387
- Analytical Chemistry 265
- Automotive Engineering 289
- Ecology 576
Countries citing papers authored by Lie Tang
This map shows the geographic impact of Lie Tang'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 Lie Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lie Tang more than expected).
Fields of papers citing papers by Lie Tang
This network shows the impact of papers produced by Lie Tang. 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 Lie Tang. The network helps show where Lie Tang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lie Tang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 3 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 30 | |
| 6 | 2022 | 18 | |
| 7 | 2021 | 13 | |
| 8 | 2020 | 26 | |
| 9 | 2019 | 13 | |
| 10 | 2018 | 62 | |
| 11 | 2016 | 9 | |
| 12 | 2016 | 2 | |
| 13 | 2015 | 14 | |
| 14 | 2014 | 17 | |
| 15 | Decomposition of Agricultural Tasks into Robotic Behaviours | 2007 | 16 |
| 16 | 2007 | 33 | |
| 17 | 2006 | 1 | |
| 18 | 2005 | 2 | |
| 19 | Systems Requirements for a Small Autonomous Tractor | 2004 | 4 |
| 20 | 2004 | 23 |
About Lie Tang
Lie Tang is a scholar working on Environmental Engineering, Plant Science, Analytical Chemistry, Ecology and Civil and Structural Engineering, having authored 111 papers that have together received 2.7k indexed citations. Recurring topics across this work include Smart Agriculture and AI (48 papers), Remote Sensing and LiDAR Applications (23 papers), Remote Sensing in Agriculture (22 papers), Leaf Properties and Growth Measurement (20 papers), Soil Mechanics and Vehicle Dynamics (16 papers), Spectroscopy and Chemometric Analyses (11 papers), Greenhouse Technology and Climate Control (10 papers) and Iterative Learning Control Systems (7 papers). The work is most often cited by research in Plant Science (1.3k citations), Environmental Engineering (387 citations), Analytical Chemistry (265 citations), Automotive Engineering (289 citations) and Ecology (576 citations). Lie Tang has collaborated with scholars based in United States, China and Netherlands. Frequent co-authors include Robert G. Landers, Brian L. Steward, Jingyao Gai, Yin Bao, Jian Jin, Patrick S. Schnable, Lirong Xiang, Maria G. Salas Fernandez, Lei Tian and Ji Li. Their work appears in journals such as Computers and Electronics in Agriculture, Journal of Manufacturing Science and Engineering, Transactions of the ASABE, Journal of Field Robotics and Biosystems Engineering.
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.