Shota Teramoto

521 total citations
22 papers, 301 citations indexed

About

Shota Teramoto is a scholar working on Plant Science, Genetics and Environmental Engineering. According to data from OpenAlex, Shota Teramoto has authored 22 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Plant Science, 6 papers in Genetics and 5 papers in Environmental Engineering. Recurrent topics in Shota Teramoto's work include Plant nutrient uptake and metabolism (14 papers), Rice Cultivation and Yield Improvement (12 papers) and Genetic Mapping and Diversity in Plants and Animals (6 papers). Shota Teramoto is often cited by papers focused on Plant nutrient uptake and metabolism (14 papers), Rice Cultivation and Yield Improvement (12 papers) and Genetic Mapping and Diversity in Plants and Animals (6 papers). Shota Teramoto collaborates with scholars based in Japan and United States. Shota Teramoto's co-authors include Yusaku Uga, Yuka Kitomi, Takanari Tanabata, Satoko Takayasu, Yumiko Arai‐Sanoh, Taiji Kawakatsu, Ryo Nishijima, Takeshi Hayashi, Tsuyoshi Tanaka and Takuji Tsukiyama and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Plant Journal and PLoS Genetics.

In The Last Decade

Shota Teramoto

21 papers receiving 298 citations

Peers

Shota Teramoto

GENET

Ming‐Hsin Lai Taiwan

Alexander Putz Germany

Johannes Kochs Germany

Anna Galinski Germany

Guriqbal Singh Dhillon India

Eric Rogers United States

Gustavo Pereyra Irujo Argentina

Péter Mikó Hungary

Matheus Baseggio United States

Lingxiao Zhu China

Ming‐Hsin Lai Taiwan

Shota Teramoto

295 ×1.2

39 ×0.7

GENET

83 ×1.7

31 ×0.8

7 ×0.3

Citations per year, relative to Shota Teramoto Shota Teramoto (= 1×) peers Ming‐Hsin Lai

Countries citing papers authored by Shota Teramoto

Since Specialization

Citations

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

Fields of papers citing papers by Shota Teramoto

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shota Teramoto

This figure shows the co-authorship network connecting the top 25 collaborators of Shota Teramoto. A scholar is included among the top collaborators of Shota Teramoto 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 Shota Teramoto. Shota Teramoto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Teramoto, Shota, et al.. (2025). Data-Driven Feedforward Control for Suppressing Coupled Vibrations in Positioning Systems. 1–7.

Teramoto, Shota & Yusaku Uga. (2024). Detection of quantitative trait loci for rice root systems grown in paddies based on nondestructive phenotyping using X‐ray computed tomography. The Plant Journal. 121(1). e17171–e17171. 4 indexed citations

Teramoto, Shota & Yusaku Uga. (2024). Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels. Plant Methods. 20(1). 73–73. 3 indexed citations

Miyoshi, Yuta, Fumiyuki Soma, Yong‐Gen Yin, et al.. (2023). Rice immediately adapts the dynamics of photosynthates translocation to roots in response to changes in soil water environment. Frontiers in Plant Science. 13. 1024144–1024144. 7 indexed citations

Teramoto, Shota, et al.. (2023). Deep learning-based soil compaction monitoring: A proof-of-concept study. Journal of Terramechanics. 111. 65–72. 3 indexed citations

Wei, Shu‐Jun, R. Tanaka, Taiji Kawakatsu, et al.. (2023). Genome- and Transcriptome-wide Association Studies to Discover Candidate Genes for Diverse Root Phenotypes in Cultivated Rice. Rice. 16(1). 55–55. 6 indexed citations

Takahashi, Yu, Hiroaki Sakai, H Ariga, et al.. (2023). Domesticating Vigna stipulacea: Chromosome-Level genome assembly reveals VsPSAT1 as a candidate gene decreasing hard-seededness. Frontiers in Plant Science. 14. 1119625–1119625. 6 indexed citations

Teramoto, Shota & Yusaku Uga. (2022). Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis by backward prediction. Plant Methods. 18(1). 133–133. 3 indexed citations

Teramoto, Shota & Yusaku Uga. (2022). Improving the efficiency of plant root system phenotyping through digitization and automation. Breeding Science. 72(1). 48–55. 12 indexed citations

10.

Teramoto, Shota, Takanari Tanabata, & Yusaku Uga. (2021). RSAtrace3D: robust vectorization software for measuring monocot root system architecture. BMC Plant Biology. 21(1). 398–398. 17 indexed citations

11.

Teramoto, Shota, Atsushi Hayashi, Ryo Nishijima, et al.. (2021). iPOTs: Internet of Things‐based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress. The Plant Journal. 107(5). 1569–1580. 15 indexed citations

12.

Kawakatsu, Taiji, Shota Teramoto, Satoko Takayasu, et al.. (2021). The transcriptomic landscapes of rice cultivars with diverse root system architectures grown in upland field conditions. The Plant Journal. 106(4). 1177–1190. 17 indexed citations

13.

Tanaka, Tsuyoshi, Ryo Nishijima, Shota Teramoto, et al.. (2020). De novoGenome Assembly of theindicaRice Variety IR64 Using Linked-Read Sequencing and Nanopore Sequencing. G3 Genes Genomes Genetics. 10(5). 1495–1501. 23 indexed citations

14.

Teramoto, Shota, Satoko Takayasu, Yuka Kitomi, et al.. (2020). High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography. Plant Methods. 16(1). 66–66. 85 indexed citations

15.

Teramoto, Shota & Yusaku Uga. (2020). A Deep Learning-Based Phenotypic Analysis of Rice Root Distribution from Field Images. Plant Phenomics. 2020. 3194308–3194308. 33 indexed citations

16.

Teramoto, Shota, Naoki Kawachi, Takanari Tanabata, et al.. (2019). Towards a deeper integrated multi-omics approach in the root system to develop climate-resilient rice. Molecular Breeding. 39(12). 18 indexed citations

17.

Teramoto, Shota, et al.. (2019). Backhoe-assisted monolith method for plant root phenotyping under upland conditions. Breeding Science. 69(3). 508–513. 17 indexed citations

18.

Teramoto, Shota, Takuji Tsukiyama, Yutaka Okumoto, & Takatoshi Tanisaka. (2014). Early Embryogenesis-Specific Expression of the Rice Transposon Ping Enhances Amplification of the MITE mPing. PLoS Genetics. 10(6). e1004396–e1004396. 9 indexed citations

19.

Tsukiyama, Takuji, Shota Teramoto, Kanako Yasuda, et al.. (2013). Loss-of-Function of a Ubiquitin-Related Modifier Promotes the Mobilization of the Active MITE mPing. Molecular Plant. 6(3). 790–801. 10 indexed citations

20.

Teramoto, Shota & Tomoaki Ohtsuki. (2005). Optical Wireless Sensor Network System Using Corner Cube Retroreflectors. SHILAP Revista de lepidopterología. 2 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.

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