Tingli Su

2.4k citations
76 papers · 1.7k · 1 hit paper · h-index 21

Impact in

Papers in

Tingli Su

70 papers receiving 1.7k citations

Tingli Su's Hit Papers

CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture 2019 · 321 citations
3210+2+4Years since publication100200300

Peers

Tingli Su
Comparison fields: 5 of 133
  • Environmental Engineering 274
  • Artificial Intelligence 437
  • Control and Systems Engineering 292
  • Signal Processing 119
  • Plant Science 376
Replace Yuting Bai with:
Yuting Bai China
Xuebo Jin China
Jianlei Kong China
Xiaoyi Wang China
Serge Guillaume France
Deepak Gupta India
Jia Wu China
Wenjian Wang China
Jing Zhou China
Jiankai Xue China
Tingli Su relative to Yuting Bai China Yuting Bai's profile →
Citations per field
00.5×
Yuting Bai · 1×
Citations per year

Countries citing papers authored by Tingli Su

Since Specialization
Citations

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

Fields of papers citing papers by Tingli Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Tingli Su, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tingli Su Line = papers co-authored together Tingli Su links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1
CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture
Hit paper breakdown →
2019321
2 2021127
3 202298
4 202188
5 202084
6 202077
7 202272
8 202368
9 202366
10 202261
11 201955
12 202349
13 201843
14 202038
15 202338
16 202136
17 201634
18 202032
19 201931
20 201923

About Tingli Su

Tingli Su is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Control and Systems Engineering, Aerospace Engineering and Computer Vision and Pattern Recognition, having authored 76 papers that have together received 1.7k indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (15 papers), Energy Load and Power Forecasting (12 papers), Indoor and Outdoor Localization Technologies (12 papers), Air Quality Monitoring and Forecasting (9 papers), Inertial Sensor and Navigation (8 papers), Fault Detection and Control Systems (7 papers), Time Series Analysis and Forecasting (7 papers) and Neural Networks and Applications (7 papers). The work is most often cited by research in Environmental Engineering (274 citations), Artificial Intelligence (437 citations), Control and Systems Engineering (292 citations), Signal Processing (119 citations) and Plant Science (376 citations). Tingli Su has collaborated with scholars based in China, United Kingdom and Taiwan. Frequent co-authors include Jianlei Kong, Xuebo Jin, Yuting Bai, Xiaoyi Wang, Yangyang Zheng, Min Zuo, Xiaoyi Wang, Xuebo Jin, Prąsun Chakrabarti and Bin Yan. Their work appears in journals such as Sensors, Applied Sciences, Complexity, Agronomy and International Journal of Robust and Nonlinear Control.

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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