Lie Tang

3.6k total citations
111 papers, 2.7k citations indexed

About

Lie Tang is a scholar working on Plant Science, Mechanical Engineering and Ecology. According to data from OpenAlex, Lie Tang has authored 111 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Plant Science, 24 papers in Mechanical Engineering and 23 papers in Ecology. Recurrent topics in Lie Tang's work include Smart Agriculture and AI (48 papers), Remote Sensing and LiDAR Applications (23 papers) and Remote Sensing in Agriculture (22 papers). Lie Tang is often cited by papers focused on Smart Agriculture and AI (48 papers), Remote Sensing and LiDAR Applications (23 papers) and Remote Sensing in Agriculture (22 papers). Lie Tang collaborates with scholars based in United States, China and Netherlands. Lie Tang's 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 and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLANT PHYSIOLOGY and The Plant Journal.

In The Last Decade

Lie Tang

107 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lie Tang United States	31	1.3k	627	576	387	327	111	2.7k
Noboru Noguchi Japan	27	1.3k 1.0×	534 0.9×	251 0.4×	255 0.7×	368 1.1×	208	2.6k
John F. Reid United States	24	1.2k 0.9×	364 0.6×	294 0.5×	192 0.5×	258 0.8×	126	2.2k
Jinya Su United Kingdom	33	832 0.6×	245 0.4×	603 1.0×	289 0.7×	1.2k 3.6×	91	3.1k
Stavros Vougioukas United States	27	1.1k 0.8×	410 0.7×	210 0.4×	174 0.4×	204 0.6×	107	2.1k
Tristán Pérez Australia	30	1.5k 1.2×	395 0.6×	327 0.6×	284 0.7×	1.2k 3.7×	135	4.2k
Jordi Palacín Spain	26	683 0.5×	271 0.4×	436 0.8×	576 1.5×	295 0.9×	101	2.2k
Manoj Karkee United States	33	3.5k 2.6×	1.1k 1.7×	628 1.1×	527 1.4×	191 0.6×	181	4.5k
Yunchao Tang China	42	2.1k 1.6×	531 0.8×	291 0.5×	347 0.9×	164 0.5×	92	4.9k
Giulio Reina Italy	28	667 0.5×	502 0.8×	173 0.3×	251 0.6×	462 1.4×	139	2.4k
En Li China	24	1.4k 1.1×	627 1.0×	198 0.3×	217 0.6×	249 0.8×	113	3.4k

Countries citing papers authored by Lie Tang

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Lie Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Lie Tang. A scholar is included among the top collaborators of Lie Tang 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 Lie Tang. Lie Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Gai, Jingyao, et al.. (2025). FSEA: Incorporating domain-specific prior knowledge for few-shot weed detection. Plant Phenomics. 7(3). 100086–100086.

Lü, Minfeng, et al.. (2024). An ensemble approach for research article classification: a case study in artificial intelligence. PeerJ Computer Science. 10. e2521–e2521. 1 indexed citations

Bao, Yin, et al.. (2024). Genetics of canopy architecture dynamics in photoperiod‐sensitive and photoperiod‐insensitive sorghum. SHILAP Revista de lepidopterología. 7(1). 3 indexed citations

Gai, Jingyao, et al.. (2024). Robust crop row detection using discrete Fourier transform (DFT) for vision-based in-field navigation. Computers and Electronics in Agriculture. 229. 109666–109666. 3 indexed citations

Xiang, Lirong, Jingyao Gai, Yin Bao, et al.. (2023). Field‐based robotic leaf angle detection and characterization of maize plants using stereo vision and deep convolutional neural networks. Journal of Field Robotics. 40(5). 1034–1053. 30 indexed citations

Bai, Xiulin, Hui Fang, Yong He, et al.. (2022). Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data. Plant Phenomics. 5. 19–19. 18 indexed citations

Xiang, Lirong, Trevor M. Nolan, Yin Bao, et al.. (2021). Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses. The Plant Journal. 107(6). 1837–1853. 13 indexed citations

Bao, Yin, et al.. (2020). Toward “Smart Canopy” Sorghum: Discovery of the Genetic Control of Leaf Angle Across Layers. PLANT PHYSIOLOGY. 184(4). 1927–1940. 26 indexed citations

Bao, Yin, Dylan Shah, Darwin A. Campbell, et al.. (2019). Assessing plant performance in the Enviratron. Plant Methods. 15(1). 117–117. 13 indexed citations

10.

Bao, Yin, Lie Tang, Srikant Srinivasan, & Patrick S. Schnable. (2018). Field-based architectural traits characterisation of maize plant using time-of-flight 3D imaging. Biosystems Engineering. 178. 86–101. 62 indexed citations

11.

Wang, Gang, Honglei Jia, Lie Tang, et al.. (2016). Standing corn residue effects on soil frost depth, snow depth and soil heat flux in Northeast China. Soil and Tillage Research. 165. 88–94. 9 indexed citations

12.

Bao, Yin, Lie Tang, Patrick S. Schnable, & Maria G. Salas Fernandez. (2016). Infield Biomass Sorghum Yield Component Traits Extraction Pipeline Using Stereo Vision. Iowa State University Digital Repository (Iowa State University). 2 indexed citations

13.

Gai, Jingyao, Lie Tang, & Brian L. Steward. (2015). Plant Recognition through the Fusion of 2D and 3D Images for Robotic Weeding. 2015 ASABE International Meeting. 14 indexed citations

14.

Tang, Lie, et al.. (2014). Within-row spacing sensing of maize plants using 3D computer vision. Biosystems Engineering. 125. 54–64. 17 indexed citations

15.

Fountas, S., Simon Blackmore, Stavros Vougioukas, et al.. (2007). Decomposition of Agricultural Tasks into Robotic Behaviours. University of Southern Denmark Research Portal (University of Southern Denmark). 16 indexed citations

16.

Tang, Lie, et al.. (2007). Colour based detection of volunteer potatoes as weeds in sugar beet fields using machine vision. Precision Agriculture. 8(6). 267–278. 33 indexed citations

17.

Khot, Lav R., et al.. (2006). Sensor Fusion for Roll and Pitch Estimation Improvement of an Agricultural Sprayer Vehicle. 2006 Portland, Oregon, July 9-12, 2006. 1 indexed citations

18.

Tang, Lie, et al.. (2005). Vision based detection of volunteer potatoes as weeds in sugar beet and cereal fields. Socio-Environmental Systems Modeling. 1. 175–182. 2 indexed citations

19.

Blackmore, Simon, S. Fountas, Lie Tang, & H. Have. (2004). Systems Requirements for a Small Autonomous Tractor. eCommons (Cornell University). 4 indexed citations

20.

Thorp, Kelly R., Lei Tian, Haibo Yao, & Lie Tang. (2004). NARROW-BAND AND DERIVATIVE-BASED VEGETATION INDICES FOR HYPERSPECTRAL DATA. Transactions of the ASAE. 47(1). 291–299. 23 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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