Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

Singhal, Rajneesh; Izquierdo, Paulo; Ranaweera, Thilanka; Segura Abá, Kenia; Brown, Brianna N.I.; Lehti-Shiu, Melissa D.; Shiu, Shin-Han

doi:10.1098/rstb.2024.0252

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

Journal Article · Thu May 29 00:00:00 EDT 2025 · Philosophical Transactions of the Royal Society B: Biological Sciences

DOI:https://doi.org/10.1098/rstb.2024.0252· OSTI ID:2588593

^[1]; Izquierdo, Paulo ^[2]; Ranaweera, Thilanka ^[2]; Segura Abá, Kenia ^[3]; Brown, Brianna N.I. ^[1]; Lehti-Shiu, Melissa D. ^[1]; Shiu, Shin-Han ^[2]

Michigan State Univ., East Lansing, MI (United States)
Michigan State Univ., East Lansing, MI (United States); Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)
Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States); Michigan State Univ., East Lansing, MI (United States)

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’.

View Accepted Manuscript (DOE)

Research Organization:: Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)

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

Grant/Contract Number:: SC0018409

OSTI ID:: 2588593

Journal Information:: Philosophical Transactions of the Royal Society B: Biological Sciences, Journal Name: Philosophical Transactions of the Royal Society B: Biological Sciences Journal Issue: 1927 Vol. 380; ISSN 0962-8436; ISSN 1471-2970

Publisher:: The Royal SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Biotechnological Advances to Improve Abiotic Stress Tolerance in Crops Villalobos-López, Miguel Angel; Arroyo-Becerra, Analilia; Quintero-Jiménez, Anareli International Journal of Molecular Sciences, Vol. 23, Issue 19 https://doi.org/10.3390/ijms231912053	journal	October 2022
QTL and Candidate Genes: Techniques and Advancement in Abiotic Stress Resistance Breeding of Major Cereals Raj, Sujitra Raj Genga; Nadarajah, Kalaivani International Journal of Molecular Sciences, Vol. 24, Issue 1 https://doi.org/10.3390/ijms24010006	journal	December 2022
Plants’ Response to Abiotic Stress: Mechanisms and Strategies Zhang, Yan; Xu, Jing; Li, Ruofan International Journal of Molecular Sciences, Vol. 24, Issue 13 https://doi.org/10.3390/ijms241310915	journal	June 2023
Stress-Induced Volatile Emissions and Signalling in Inter-Plant Communication Midzi, Joanah; Jeffery, David W.; Baumann, Ute Plants, Vol. 11, Issue 19, p. 2566 https://doi.org/10.3390/plants11192566	journal	September 2022

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