Jason Kulk
Impact in
- Plant Science top 10%
- Smart Agriculture and AI
- Plant Disease Management Techniques
- Weed Control and Herbicide Applications
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- Remote Sensing in Agriculture
Papers in
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- Smart Agriculture and AI 3
- Weed Control and Herbicide Applications 2
- Nematode management and characterization studies 2
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- Robotic Locomotion and Control 4
- Prosthetics and Rehabilitation Robotics 3
- Co-authors
- Chris McCool (4 shared papers)Tristán Pérez (3 shared papers)Feras Dayoub (2 shared papers)R. Russell (2 shared papers)Chris Lehnert (2 shared papers)Andrew English (1 shared paper)James S. Welsh (4 shared papers)Jennifer Firn (1 shared paper)
- Journals
- Journal of Field Robotics (1 paper)Architectural Science Review (1 paper)IEEE Robotics and Automation Letters (1 paper)Frontiers in Neurology (1 paper)International Conference on Robotics and Automation (1 paper)
- Partner nations
- AustraliaUnited States
In The Last Decade
Jason Kulk
12 papers receiving 289 citations
Peers
Comparison fields: 5 of 58
- Plant Science 209
- Ecology 42
- Environmental Engineering 19
- Control and Systems Engineering 30
- Computer Vision and Pattern Recognition 25
Countries citing papers authored by Jason Kulk
This map shows the geographic impact of Jason Kulk'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 Jason Kulk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Kulk more than expected).
Fields of papers citing papers by Jason Kulk
This network shows the impact of papers produced by Jason Kulk. 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 Jason Kulk. The network helps show where Jason Kulk may publish in the future.
Co-authors
The 22 scholars most cited alongside Jason Kulk, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 148 | |
| 2 | 2018 | 53 | |
| 3 | 2017 | 29 | |
| 4 | 2014 | 25 | |
| 5 | A low power walk for the NAO robot | 2008 | 19 |
| 6 | 2011 | 11 | |
| 7 | 2012 | 6 | |
| 8 | 2019 | 4 | |
| 9 | 2011 | 3 | |
| 10 | The 2008 NUManoids Team Report | 2008 | 2 |
| 11 | 2017 | 1 | |
| 12 | 2010 | 1 | |
| 13 | Humanoid robots for modelling and analysing visual gaze dynamics of pedestrians moving in urban space | 2011 | 0 |
| 14 | The NUbots' Team Description for 2011 | 2011 | 0 |
About Jason Kulk
Jason Kulk is a scholar working on Plant Science, Biomedical Engineering, Molecular Biology, Computer Vision and Pattern Recognition and Mechanical Engineering, having authored 14 papers that have together received 302 indexed citations. Recurring topics across this work include Robotic Locomotion and Control (4 papers), Prosthetics and Rehabilitation Robotics (3 papers), Smart Agriculture and AI (3 papers), Weed Control and Herbicide Applications (2 papers), Biological Control of Invasive Species (2 papers), Muscle Physiology and Disorders (2 papers), Nematode management and characterization studies (2 papers) and Stroke Rehabilitation and Recovery (1 paper). The work is most often cited by research in Plant Science (209 citations), Ecology (42 citations), Environmental Engineering (19 citations), Control and Systems Engineering (30 citations) and Computer Vision and Pattern Recognition (25 citations). Jason Kulk has collaborated with scholars based in Australia and United States. Frequent co-authors include Chris McCool, Tristán Pérez, Feras Dayoub, R. Russell, Chris Lehnert, Andrew English, James S. Welsh, Jennifer Firn, David Hall and James R. Beattie. Their work appears in journals such as Journal of Field Robotics, Architectural Science Review, IEEE Robotics and Automation Letters, Frontiers in Neurology and International Conference on Robotics and Automation.
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.