Sue Han Lee

1.3k total citations · 2 hit papers
15 papers, 860 citations indexed

About

Sue Han Lee is a scholar working on Plant Science, Ecology and Pharmacology. According to data from OpenAlex, Sue Han Lee has authored 15 papers receiving a total of 860 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Plant Science, 6 papers in Ecology and 3 papers in Pharmacology. Recurrent topics in Sue Han Lee's work include Smart Agriculture and AI (12 papers), Remote Sensing in Agriculture (6 papers) and Plant Virus Research Studies (5 papers). Sue Han Lee is often cited by papers focused on Smart Agriculture and AI (12 papers), Remote Sensing in Agriculture (6 papers) and Plant Virus Research Studies (5 papers). Sue Han Lee collaborates with scholars based in Malaysia, France and Italy. Sue Han Lee's co-authors include Chee Seng Chan, Paolo Remagnino, Simon Joseph Mayo, Alexis Joly, Pierre Bonnet, Hervé Goëau, Dongming Zhang, Chung Siung Choo, Dominic Ek Leong Ong and Patrick Then and has published in prestigious journals such as IEEE Transactions on Image Processing, Frontiers in Plant Science and Pattern Recognition.

In The Last Decade

Sue Han Lee

14 papers receiving 797 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

How deep learning extracts and learns leaf features for plant classification

2017 415 citations Sue Han Lee, Chee Seng Chan et al. Pattern Recognition profile →
New perspectives on plant disease characterization based on deep learning

2020 226 citations Sue Han Lee, Hervé Goëau et al. Computers and Electronics in Agriculture profile →

Peers

Sue Han Lee

PS

AC

ECOLO

CVPR

MB

Mónica G. Larese Argentina

Edna C. Too Kenya

Anand Koirala Australia

Thiago Teixeira Santos Brazil

Aravind Krishnaswamy Rangarajan India

Henrik Skov Midtiby Denmark

Ranjan Parekh India

Suraj Amatya United States

J. Arun Pandian India

Daeun Choi United States

Mónica G. Larese Argentina

Sue Han Lee

664 ×0.9

PS

251 ×0.9

AC

192 ×1.1

ECOLO

96 ×1.5

CVPR

44 ×1.3

MB

Citations per year, relative to Sue Han Lee Sue Han Lee (= 1×) peers Mónica G. Larese

Countries citing papers authored by Sue Han Lee

Since Specialization

Citations

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

Fields of papers citing papers by Sue Han Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sue Han Lee. 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 Sue Han Lee. The network helps show where Sue Han Lee may publish in the future.

Co-authorship network of co-authors of Sue Han Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Sue Han Lee. A scholar is included among the top collaborators of Sue Han Lee 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 Sue Han Lee. Sue Han Lee 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:

15 of 15 papers shown

1.

Lee, Sue Han, et al.. (2025). PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification. Smart Agricultural Technology. 10. 100813–100813. 3 indexed citations

2.

Lee, Sue Han, et al.. (2024). Revolutionizing Plant Pathogen Conservation: The Past, Present, and Future of AI in Preserving Natural Ecosystems. Biodiversity Information Science and Standards. 8.

3.

Lee, Sue Han, et al.. (2024). Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition. Neurocomputing. 610. 128608–128608. 6 indexed citations

4.

Lee, Sue Han, et al.. (2024). Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning. Geosciences. 14(3). 55–55. 3 indexed citations

5.

Lee, Sue Han, et al.. (2023). Pairwise Feature Learning for Unseen Plant Disease Recognition. Unimas Institutional Repository (Universiti Malaysia Sarawak). 306–310. 2 indexed citations

6.

Lee, Sue Han, et al.. (2023). Unveiling Robust Feature Spaces: Image vs. Embedding-Oriented Approaches for Plant Disease Identification. 666–673. 1 indexed citations

7.

Choo, Chung Siung, et al.. (2023). Feature Visualization Using a Deep Learning Technique with Attention-Based Mechanism for Pipe Jacking through “Soft Rocks”. 42–54. 1 indexed citations

8.

Lee, Sue Han, et al.. (2022). A machine learning approach for cross-domain plant identification using herbarium specimens. Neural Computing and Applications. 35(8). 5963–5985. 26 indexed citations

9.

Lee, Sue Han, Hervé Goëau, Pierre Bonnet, & Alexis Joly. (2020). Attention-Based Recurrent Neural Network for Plant Disease Classification. Frontiers in Plant Science. 11. 601250–601250. 60 indexed citations

10.

Lee, Sue Han, Hervé Goëau, Pierre Bonnet, & Alexis Joly. (2020). New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture. 170. 105220–105220. 226 indexed citations breakdown →

11.

Lee, Sue Han, Chee Seng Chan, & Paolo Remagnino. (2018). Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks. IEEE Transactions on Image Processing. 27(9). 4287–4301. 79 indexed citations

12.

Lee, Sue Han, et al.. (2017). LifeClef 2017 Plant Identification Challenge: Classifying Plants using Generic-Organ Correlation Features.. CLEF (Working Notes). 2 indexed citations

13.

Lee, Sue Han, et al.. (2017). HGO-CNN: Hybrid generic-organ convolutional neural network for multi-organ plant classification. 190. 4462–4466. 12 indexed citations

14.

Lee, Sue Han, Chee Seng Chan, Simon Joseph Mayo, & Paolo Remagnino. (2017). How deep learning extracts and learns leaf features for plant classification. Pattern Recognition. 71. 1–13. 415 indexed citations breakdown →

15.

Lee, Sue Han, et al.. (2016). Plant Identification System based on a Convolutional Neural Network for the LifeClef 2016 Plant Classification Task.. CLEF (Working Notes). 502–510. 24 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.

Explore authors with similar magnitude of impact