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

来源：

PLANT PHENOMICS | 2024年 / 6卷

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

页数：16

共 112 条

[1] Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey [J].

Akhtar, Naveed ;

Mian, Ajmal .

IEEE ACCESS, 2018, 6 :14410-14430

[2]

Ameen A., 2017, International Journal of Advances in Scientific Research, V3, P129, DOI [10.7439/ijasr.v3i12.4410, DOI 10.7439/IJASR.V3I12.4410]

[3]

Arsenault J. L., 1995, Hortscience, V30, P906

[4] Uncovering the hidden half of plants using new advances in root phenotyping [J].

Atkinson, Jonathan A. ;

Pound, Michael P. ;

Bennett, Malcolm J. ;

Wells, Darren M. .

CURRENT OPINION IN BIOTECHNOLOGY, 2019, 55 :1-8

[5]

Bai YC, 2023, Arxiv, DOI [arXiv:2311.03566, 10.48550/arXiv.2311.03566, DOI 10.48550/ARXIV.2311.03566]

[6] Deep-Rooted Plant Species Recruit Distinct Bacterial Communities in the Subsoil [J].

Bak, Frederik ;

Lyhne-Kjaerbye, Annemette ;

Tardif, Stacie ;

Dresboll, Dorte Bodin ;

Nybroe, Ole ;

Nicolaisen, Mette H. .

PHYTOBIOMES JOURNAL, 2022, 6 (03) :236-246

[7] Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline [J].

Bauer, Felix Maximilian ;

Laerm, Lena ;

Morandage, Shehan ;

Lobet, Guillaume ;

Vanderborght, Jan ;

Vereecken, Harry ;

Schnepf, Andrea .

PLANT PHENOMICS, 2022, 2022

[8] Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models [J].

Baykalov, Pavel ;

Bussmann, Bart ;

Nair, Richard ;

Smith, Abraham George ;

Bodner, Gernot ;

Hadar, Ofer ;

Lazarovitch, Naftali ;

Rewald, Boris .

PLANT METHODS, 2023, 19 (01)

[9] Understanding and reducing lodging in cereals [J].

Berry, PM ;

Sterling, M ;

Spink, JH ;

Baker, CJ ;

Sylvester-Bradley, R ;

Mooney, SJ ;

Tams, AR ;

Ennos, AR .

ADVANCES IN AGRONOMY, VOL 84, 2004, 84 :217-271

[10]

Bishopp A, 2015, NAT PLANTS, V1, DOI [10.1038/NPLANTS.2015.117, 10.1038/nplants.2015.117]

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