The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Weihs, Brandon J.; Heuschele, Deborah-Jo; Tang, Zhou; York, Larry M.; Zhang, Zhiwu; Xu, Zhanyou

doi:10.34133/plantphenomics.0178

Title: The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Journal Article · 17 April 2024 · Plant Phenomics

DOI: https://doi.org/10.34133/plantphenomics.0178 · OSTI ID:2371104

^[1];

^[1]; Tang, Zhou ^[2];

^[3]; Zhang, Zhiwu ^[2];

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US Dept. of Agriculture (USDA), St. Paul, MN (United States). Agricultural Research Service (ARS). Plant Science Research; Univ. of Minnesota, St. Paul, MN (United States)
Washington State Univ., Pullman, WA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Bioenergy Innovation (CBI)
US Dept. of Agriculture (USDA), St. Paul, MN (United States). Agricultural Research Service (ARS). Plant Science Research

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDA; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2371104

Journal Information:: Plant Phenomics, Journal Name: Plant Phenomics Vol. 6; ISSN 2643-6515

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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