Yue Shi

4.3k citations
70 papers · 3.0k indexed · 2 hit papers · h-index 26

Yue Shi

67 papers receiving 2.9k citations

Hit Papers

A Deep Learning-Based Approach for Automated Yellow Rus...2442015202620182022200400600

Peers

Yue Shi
Comparison fields: 5 of 125
  • Soil Science 536
  • Ecology 1.4k
  • Analytical Chemistry 420
  • Plant Science 1.2k
  • Nature and Landscape Conservation 377
Replace Paulo Eduardo Teodoro with:
Paulo Eduardo Teodoro Brazil
Aditya Singh United States
Thomas Udelhoven Germany
Dan S. Long United States
Felix Fritschi United States
Megan Lewis Australia
Mário Cunha Portugal
Wouter H. Maes Belgium
V. González-Dugo Spain
Thomas Alexandridis Greece
Yue Shi relative to Paulo Eduardo Teodoro Brazil Paulo Eduardo Teodoro's profile →
Citations per field
00.5×1.5×
Paulo Eduardo Teodoro · 1×
Citations per year

Countries citing papers authored by Yue Shi

Since Specialization
Citations

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

Fields of papers citing papers by Yue Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Yue Shi, 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 Yue Shi Line = papers co-authored together Yue Shi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 202418
3 20230
4 20236
5 202115
6 202153
7 202035
8 201915
9 201943
10 201964
11
A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Imagesbreakdown →
2019244
12 201913
13 20182
14 201763
15 201662
16
The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climatebreakdown →
2015644
17 201464
18 201317
19 2012129
20 201213

About Yue Shi

Yue Shi is a scholar working on Ecological Modeling, Analytical Chemistry and Environmental Engineering, having authored 70 papers that have together received 3.0k indexed citations. Recurring topics across this work include Remote Sensing in Agriculture (21 papers), Spectroscopy and Chemometric Analyses (10 papers), Smart Agriculture and AI (7 papers), Soil Carbon and Nitrogen Dynamics (6 papers), Species Distribution and Climate Change (5 papers), Flood Risk Assessment and Management (5 papers), Land Use and Ecosystem Services (5 papers) and Leaf Properties and Growth Measurement (5 papers). The work is most often cited by research in Soil Science (536 citations), Ecology (1.4k citations) and Analytical Chemistry (420 citations). Yue Shi has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Jin He, Wenjiang Huang, Yingying Dong, Litong Chen, Xin Jing, Huichun Ye, Haiyan Chu, Aimée T. Classen, Yu Shi and Youxu Jiang. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and The Science of The Total Environment.

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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