Review Article | Open Access

Volume 2024 |Article ID 0178 | https://doi.org/10.34133/plantphenomics.0178

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

Brandon J. Weihs,^1,2 Deborah-Jo Heuschele,^1,2 Zhou Tang,³ Larry M. York,⁴ Zhiwu Zhang,³ Zhanyou Xu ^1,2

¹United States Department of Agriculture–Agricultural Research Service–Plant Science Research, St. Paul, MN 55108, USA
²Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
³Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
⁴Biosciences Division and Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA

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.