Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective-Reference-Cited by-同舟云学术

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Published:2018-12-06 Issue:1 Volume:8 Page:
ISSN:2047-217X
Container-title:GigaScience
language:en
Short-container-title:

Author:

Mochida Keiichi¹²³⁴⁵^ORCID,Koda Satoru⁶^ORCID,Inoue Komaki¹^ORCID,Hirayama Takashi³^ORCID,Tanaka Shojiro⁷^ORCID,Nishii Ryuei⁸^ORCID,Melgani Farid⁹^ORCID

Affiliation:

1. Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

2. Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

3. Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan

4. Kihara Institute for Biological Research, Yokohama City University, 641-12 Maioka-cho, Totsuka-ku, Yokohama, Kanagawa 244–0813, Japan

5. Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan

6. Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

7. Hiroshima University of Economics, 5-37-1, Gion, Asaminami, Hiroshima-shi Hiroshima 731-0138, Japan

8. Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

9. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy

Funder

Core Research for Evolutionary Sciecne and Technology

Japan Science and Technology Agency

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

Link

http://academic.oup.com/gigascience/article-pdf/8/1/giy153/27359533/giy153.pdf

Reference131 articles.

1. Plant phenomics, from sensors to knowledge;Tardieu;Curr Biol,2017

2. Reconsidering plant memory: intersections between stress recovery, RNA turnover, and epigenetics;Crisp;Sci Adv,2016

3. Exploring genetic diversity in plants using high-throughput sequencing techniques;Onda;Curr Genomics,2016

4. Status and prospects of next generation sequencing technologies in crop plants;Sharma;Curr Issues Mol Biol,2018

5. Phenomic approaches and tools for phytopathologists;Simko;Phytopathology,2017

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