Sook Yoon

4.1k citations
74 papers · 2.7k · 2 hit papers · h-index 23

Impact in

Papers in

Sook Yoon

69 papers receiving 2.6k citations

Sook Yoon's Hit Papers

A Comprehensive Survey of Image Augmentation Techniques for Deep Learning 2023 · 319 citations
3190+3+6Years since publication2505007501000

Peers

Sook Yoon
Comparison fields: 5 of 147
  • Analytical Chemistry 456
  • Signal Processing 452
  • Plant Science 1.4k
  • Computer Vision and Pattern Recognition 606
  • Small Animals 98
Replace Dong Sun Park with:
Dong Sun Park South Korea
Jayme Garcia Arnal Barbedo Brazil
Chris McCool United States
Jayanta Kumar Basak India
Guoxiong Zhou China
Dongjian He China
Diego H. Milone Argentina
Christos Tachtatzis United Kingdom
Weizheng Shen China
Alvaro Fuentes South Korea
Sook Yoon relative to Dong Sun Park South Korea Dong Sun Park's profile →
Citations per field
00.5×3.9×
Dong Sun Park · 1×
Citations per year

Countries citing papers authored by Sook Yoon

Since Specialization
Citations

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

Fields of papers citing papers by Sook Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
Hit paper breakdown →
20171020
2
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning
Hit paper breakdown →
2023319
3 2018143
4 2013136
5 2020104
6 201379
7 201772
8 201151
9 201550
10 201244
11 202240
12 201939
13 201435
14 202333
15 202230
16 202327
17 202324
18 202224
19 201424
20 201224

About Sook Yoon

Sook Yoon is a scholar working on Computer Vision and Pattern Recognition, Plant Science, Signal Processing, Artificial Intelligence and Small Animals, having authored 74 papers that have together received 2.7k indexed citations. Recurring topics across this work include Smart Agriculture and AI (26 papers), Biometric Identification and Security (15 papers), Video Surveillance and Tracking Methods (14 papers), Face and Expression Recognition (10 papers), Plant Virus Research Studies (10 papers), Advanced Steganography and Watermarking Techniques (7 papers), Animal Behavior and Welfare Studies (7 papers) and Food Supply Chain Traceability (6 papers). The work is most often cited by research in Analytical Chemistry (456 citations), Signal Processing (452 citations), Plant Science (1.4k citations), Computer Vision and Pattern Recognition (606 citations) and Small Animals (98 citations). Sook Yoon has collaborated with scholars based in South Korea, China and United States. Frequent co-authors include Alvaro Fuentes, Dong Sun Park, Dong Jun Park, Sang Ryong Kim, Mingle Xu, Yu Lu, Shan Juan Xie, Zhihui Wang, Jae-Su Lee and Jucheng Yang. Their work appears in journals such as Frontiers in Plant Science, Computers and Electronics in Agriculture, Sensors, IEEE Access and Neural Computing and Applications.

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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