High‐throughput measurement of plant fitness traits with an object detection method using Faster R‐CNN-Reference-Cited by-同舟云学术

High‐throughput measurement of plant fitness traits with an object detection method using Faster R‐CNN

Published:2022-03-26 Issue:4 Volume:234 Page:1521-1533
ISSN:0028-646X
Container-title:New Phytologist
language:en
Short-container-title:New Phytologist

Author:

Wang Peipei¹²^ORCID,Meng Fanrui¹²^ORCID,Donaldson Paityn¹,Horan Sarah¹,Panchy Nicholas L.³^ORCID,Vischulis Elyse⁴,Winship Eamon⁵^ORCID,Conner Jeffrey K.¹⁶⁷^ORCID,Krysan Patrick J.⁸^ORCID,Shiu Shin‐Han¹²⁴⁷⁹^ORCID,Lehti‐Shiu Melissa D.¹^ORCID

Affiliation:

1. Department of Plant Biology Michigan State University East Lansing MI 48824 USA

2. DOE Great Lake Bioenergy Research Center Michigan State University East Lansing MI 48824 USA

3. National Institute for Mathematical and Biological Synthesis University of Tennessee 1122 Volunteer Blvd, Suite 106 Knoxville TN 37996‐3410 USA

4. Genetics and Genome Sciences Graduate Program Michigan State University East Lansing MI 48824 USA

5. Department of Biochemistry and Molecular Biology Michigan State University East Lansing MI 48824 USA

6. W.K. Kellogg Biological Station Michigan State University 3700 E. Gull Lake Drive Hickory Corners MI 49060 USA

7. Ecology, Evolution, and Behavior Graduate Program Michigan State University East Lansing MI 48824 USA

8. Department of Horticulture University of Wisconsin‐Madison Madison WI 53705 USA

9. Department of Computational Mathematics, Science, and Engineering Michigan State University East Lansing MI 48824 USA

Funder

National Science Foundation

Publisher

Wiley

Subject

Plant Science,Physiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18056

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