SCIENCE@home

Root Anatomy based on Root Cross-Section Image Analysis with Deep Learning

Chaoxin Wang,Xukun Li,Doina Caragea,R. Bheemanahalli,S. V. K. Jagadish

Published 2018 in bioRxiv

ABSTRACT

Background The aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has the potential to further increase food production. However, the belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical features in root cross-section images have been proposed. However, the existing tools are not fully automated and require significant human effort to produce accurate results. Automated machine learning tools have the potential to accomplish this complex task. Methods We propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images. Using the Faster Region-based Convolutional Neural Network (Faster R-CNN), the DL-RootAnatomy models detect objects such as root, stele and late metaxylem, and predict rectangular bounding boxes around such objects. Subsequently, the bounding boxes are used to estimate the root diameter, stele diameter, late metaxylem average diameter and number. Conclusions Experimental evaluation using standard object detection metrics, such as intersection-over-union and mean average precision, has shown that our models can accurately detect the root, stele and late metaxylem objects. Furthermore, the results have shown that the measurements estimated based on predicted bounding boxes have very small root mean square error when compared with the corresponding ground truth values, suggesting that DL-RootAnatomy can be used to accurately detect anatomical features. Finally, a comparison with existing approaches, which involve some degree of human interaction, has shown that the proposed approach is more accurate than existing approaches.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    bioRxiv

  • Publication date

    2018-10-15

  • Fields of study

    Biology, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1101/442244
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-76 of 76 references · Page 1 of 1

CITED BY

Showing 1-24 of 24 citing papers · Page 1 of 1