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

doi:10.1117/12.2687786

17 October 2023 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

Author Affiliations +

Proceedings Volume 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV; 1272711 (2023) https://doi.org/10.1117/12.2687786
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

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.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Divya Mishra, Sharon Chemweno, Ofer Hadar, Naftali Lazarovitch, and Jonathan E. Ephrath "Deep-learning-based image super-resolution for enhanced root hair visualization and root traits analysis", Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 1272711 (17 October 2023); https://doi.org/10.1117/12.2687786

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available