Deep-Learning Based Image Super-Resolution for Enhanced Root Hair Visualization and Root Traits Analysis

Divya Mishra, Sharon Chemweno, Ofer Hadar, Naftali Lazarovitch, Jonathan E. Ephrath

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The arrangement of plant roots and their overall structure, known as root system architecture (RSA), plays an important role in acquiring water and nutrients essential for plant growth and development. Moreover, the RSA demonstrates remarkable adaptability to environmental stresses, making it a central factor in plant adaptation. Root traits, including root length, root diameter, root length density (RLD), and the presence of root hairs, play a crucial role in optimizing resource utilization within the soil and enhancing productivity. In particular, root hairs play a crucial role in the overall health and functioning of plants. These microscopic, hair-like structures extend from the surface of root cells and greatly increase the root’s surface area, which accounts for approximately 70% of the total root area. The characteristics of root hairs, such as their length and density, significantly enhance soil nutrients and water uptake. Considering these advantages, it is difficult to observe root hairs in a scene with low resolution. Therefore, we proposed a study using deep learning-based image super-resolution methods as a pre-processing step that helps to reconstruct finer details and structures within the root hairs, leading to a more accurate representation of their morphology, to understand the improvement in the response of root hairs under different environmental conditions and their impact on nutrient and water uptake, models need to be evolved.

Original languageEnglish
Title of host publicationRemote Sensing for Agriculture, Ecosystems, and Hydrology XXV
EditorsChristopher M. Neale, Antonino Maltese
PublisherSPIE
ISBN (Electronic)9781510666832
DOIs
StatePublished - 1 Jan 2023
EventRemote Sensing for Agriculture, Ecosystems, and Hydrology XXV 2023 - Amsterdam, Netherlands
Duration: 3 Sep 20236 Sep 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12727
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceRemote Sensing for Agriculture, Ecosystems, and Hydrology XXV 2023
Country/TerritoryNetherlands
CityAmsterdam
Period3/09/236/09/23

Keywords

  • Deep learning
  • Image Super-resolution
  • Image analysis
  • Minirhizotron technique
  • Root hair analysis
  • Root hairs image enhancement
  • Root phenotyping

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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