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.
Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review
2018940 citationsSalah Sukkarieh et al.Computers and Electronics in Agricultureprofile →
Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions
Countries citing papers authored by Salah Sukkarieh
Since
Specialization
Citations
This map shows the geographic impact of Salah Sukkarieh'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 Salah Sukkarieh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Salah Sukkarieh more than expected).
This network shows the impact of papers produced by Salah Sukkarieh. 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 Salah Sukkarieh. The network helps show where Salah Sukkarieh may publish in the future.
Co-authorship network of co-authors of Salah Sukkarieh
This figure shows the co-authorship network connecting the top 25 collaborators of Salah Sukkarieh.
A scholar is included among the top collaborators of Salah Sukkarieh 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 Salah Sukkarieh. Salah Sukkarieh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hung, Calvin & Salah Sukkarieh. (2015). Using robotic aircraft and intelligent surveillance systems for orange hawkweed detection. Plant protection quarterly. 30(3). 100–102.5 indexed citations
Dugdale, Tony M., et al.. (2014). Detection of alligator weed using an unmanned aerial vehicle. Plant protection quarterly. 29(3). 84.4 indexed citations
Bryson, Mitch & Salah Sukkarieh. (2012). Vehicle Model Aided Inertial Navigation for a UAV using Low-cost Sensors.18 indexed citations
13.
Yang, Kwangjin & Salah Sukkarieh. (2012). Model predictive unified planning and control of rotary-wing unmanned aerial vehicle. International Conference on Control, Automation and Systems. 1974–1979.3 indexed citations
Corke, Peter & Salah Sukkarieh. (2006). Field and Service Robotics: Results of the 5th International Conference (Springer Tracts in Advanced Robotics). Springer eBooks.2 indexed citations
18.
Sukkarieh, Salah, et al.. (2004). Implementation of a Skewed-Redundant Low-Cost INS in a Fast-Prototyping Environment. 954–961.8 indexed citations
19.
Ridley, Matthew, et al.. (2003). Six DoF Decentralised SLAM. ANU Open Research (Australian National University).16 indexed citations
20.
Kim, Jong Hyuk & Salah Sukkarieh. (2002). Flight Test Results of GPS/INS Navigation Loop for an Autonomous Unmanned Aerial Vehicle (UAV). Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2002). 510–517.19 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.