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

被引:10
作者
Weihs, Brandon J. [1 ,2 ]
Heuschele, Deborah -Jo [1 ,2 ]
Tang, Zhou [3 ]
York, Larry M. [4 ,5 ]
Zhang, Zhiwu [3 ]
Xu, Zhanyou [1 ,2 ]
机构
[1] US Dept Agr, Agr Res Serv, Plant Sci Res Unit, St Paul, MN 55108 USA
[2] Univ Minnesota, Dept Agron & Plant Genet, St Paul, MN 55108 USA
[3] Washington State Univ, Dept Crop & Soil Sci, Pullman, WA 99164 USA
[4] Oak Ridge Natl Lab, Biosci Div, Oak Ridge, TN 37830 USA
[5] Oak Ridge Natl Lab, Ctr Bioenergy Innovat, Oak Ridge, TN 37830 USA
关键词
SEGMENTATION; GRADIENT; TRAITS; PLANTS;
D O I
10.34133/plantphenomics.0178
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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.
引用
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页数:16
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