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ARS Home » Midwest Area » St. Paul, Minnesota » Plant Science Research » Research » Publications at this Location » Publication #408931
Research Project: Genetic Improvement and Cropping Systems of Alfalfa for Livestock Utilization, Environmental Protection and Soil Health

Location: Plant Science Research

Title: The state of the art in root system architecture image analysis using artificial intelligence: A review

Author
item Weihs, Brandon
item Heuschele, Deborah - Jo
item TANG, ZHOU - Washington State University
item YORK, LARRY - Oak Ridge National Laboratory
item ZHANG, ZHIWU - Washington State University
item Xu, Zhanyou

Submitted to: Plant Phenomics
Publication Type: Review Article
Publication Acceptance Date: 3/27/2024
Publication Date: 4/18/2024
Citation: Weihs, B.J., Heuschele, D.J., Tang, Z., York, L., Zhang, Z., Xu, Z. 2024. The state of the art in root system architecture image analysis using artificial intelligence: A review. Plant Phenomics. 6. Article 0178. https://doi.org/10.34133/plantphenomics.0178.
DOI: https://doi.org/10.34133/plantphenomics.0178

Interpretive Summary: Roots are essential for securing plants in the soil and for nutrient and water uptake. They also serve important roles for perennial plants in winter survival for storing carbohydrates. In alfalfa, a perennial legume, selecting plants for specific root system architecture (RSA) traits has increased herbage yields; however, selecting plants with a desired root phenotype is challenging since scoring has relied on subjective classification and is prone to human error and bias. This article reviews the research progress, challenges, future research, and breeding perspectives in RSA and summarizes the application of machine learning and deep learning to RSA research and breeding. The review provides information for root biologists and breeders to accelerate progress in root biology and plant improvement.

Technical Abstract: Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage; however, they have studied less than the aboveground 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 paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand, 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 RSA research's origins, applications, challenges, and future directions regarding image analyses using AI.