Masahiko Suguri
- Plant Science top 10%
- Ecology top 10%
- Analytical Chemistry top 5%
- Mechanical Engineering
- Computer Vision and Pattern Recognition
- Co-authors
- Chanseok RyuMichihisa IidaMikio UmedaHiroki KuritaYang LiChungkeun LeeShigenobu KishinoTatsuya Inamura
- Topics
- Smart Agriculture and AI (19 papers)Remote Sensing in Agriculture (15 papers)Spectroscopy and Chemometric Analyses (15 papers)
- Partner nations
- JapanSouth KoreaCambodia
In The Last Decade
Masahiko Suguri
49 papers receiving 455 citations
Peers
Comparison fields: 5 of 65
- Plant Science 281
- Ecology 189
- Analytical Chemistry 139
- Mechanical Engineering 64
- Computer Vision and Pattern Recognition 49
Countries citing papers authored by Masahiko Suguri
This map shows the geographic impact of Masahiko Suguri'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 Masahiko Suguri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masahiko Suguri more than expected).
Fields of papers citing papers by Masahiko Suguri
This network shows the impact of papers produced by Masahiko Suguri. 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 Masahiko Suguri. The network helps show where Masahiko Suguri may publish in the future.
Co-authorship network of co-authors of Masahiko Suguri
This figure shows the co-authorship network connecting the top 25 collaborators of Masahiko Suguri. A scholar is included among the top collaborators of Masahiko Suguri 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 Masahiko Suguri. Masahiko Suguri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 17 | |
| 3 | 16 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | Automation of unloading work - searching and detecting a grain container and proper positioning of a harvester's spout. | 1 |
| 7 | Estimation of the quantity and quality of green tea using hyperspectral sensing. | 5 |
| 8 | 3 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 5 | |
| 12 | 4 | |
| 13 | 5 | |
| 14 | Obstacle Avoidance System for Autonomous Transportation Vehicle based on Image Processing | 13 |
| 15 | 2 | |
| 16 | 1 | |
| 17 | 1 | |
| 18 | Autonomous Transportation Vehicle Using Image Processing. (Part 2). Obstacle detection for collision avoidance.:Obstacle detection for collision avoidance | 1 |
| 19 | 8 | |
| 20 | Estimation of nitrogen content using machine vision in a paddy field. | 3 |
About Masahiko Suguri
Masahiko Suguri is a scholar working on Analytical Chemistry, Ecology and Industrial and Manufacturing Engineering, having authored 50 papers that have together received 472 indexed citations. Recurring topics across this work include Smart Agriculture and AI (19 papers), Remote Sensing in Agriculture (15 papers) and Spectroscopy and Chemometric Analyses (15 papers). The work is most often cited by research in Analytical Chemistry (139 citations), Plant Science (281 citations) and Ecology (189 citations). Masahiko Suguri has collaborated with scholars based in Japan, South Korea and Cambodia. Frequent co-authors include Chanseok Ryu, Michihisa Iida, Mikio Umeda, Hiroki Kurita, Yang Li, Chungkeun Lee, Shigenobu Kishino, Tatsuya Inamura, Si‐Bum Park and Makoto Iida. Their work appears in journals such as Food Chemistry, Field Crops Research and Computers and Electronics in Agriculture.
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.