Abstract
Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the "Second Green Revolution". To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.
| Original language | English |
|---|---|
| Article number | 0178 |
| Journal | Plant Phenomics |
| Volume | 6 |
| DOIs | |
| State | Published - Jan 2024 |
Funding
We express their sincere thanks to the editor and reviewers for valuable suggestions and comments. We also thank our colleague and unit leader, D. Samac, for advice, edits, and her interest in our success for this manuscript and in general. Funding: This study was funded by US Department of Agriculture Agricultural Research Service project 5062-12210-004-D. L.M.Y. was funded by the Center for Bioenergy Innovation (CBI), which is a US Department of Energy (DOE) Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. This manuscript has been authored in part by UT-Battelle LLC that manages Oak Ridge National Laboratory under contract DE-AC05-00OR22725 with the DOE. The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (https:// energy.gov/downloads/doe-public-access-plan). Author contributions: B.J.W.: Conceptualization, writing original draft text and graphics, review and editing, and visualization. D.-J.H.: Writing original draft text, review and editing, and funding acquisition. Z.T.: Review and editing. L.M.Y.: Review and editing and funding acquisition. Z.Z.: Review and editing. Z.X.: Conceptualization, writing original draft text, review and editing, and funding acquisition. Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. The US Department of Agriculture is an equal opportunity provider and employer. All experiments complied with the current laws of the United States, the country in which they were performed.