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Research article - Peer-reviewed, 2020

Affordable phenotyping of winter wheat under field and controlled conditions for drought tolerance

Kumar, Dhananjay; Kushwaha, Sandeep Kumar; Kushwaha, Sandeep; Delvento, Chiara; Liatukas, Žilvinas; Vivekanand, Vivekanand; Svensson, Jan T.; Henriksson, Tina; Brazauskas, Gintaras; Chawade, Aakash

Abstract

Drought stress is one of the key plant stresses reducing grain yield in cereal crops worldwide. Although it is not a breeding target in Northern Europe, the changing climate and the drought of 2018 have increased its significance in the region. A key challenge, therefore, is to identify novel germplasm with higher drought tolerance, a task that will require continuous characterization of a large number of genotypes. The aim of this work was to assess if phenotyping systems with low-cost consumer-grade digital cameras can be used to characterize germplasm for drought tolerance. To achieve this goal, we built a proximal phenotyping cart mounted with digital cameras and evaluated it by characterizing 142 winter wheat genotypes for drought tolerance under field conditions. The same genotypes were additionally characterized for seedling stage traits by imaging under controlled growth conditions. The analysis revealed that under field conditions, plant biomass, relative growth rates, and Normalized Difference Vegetation Index (NDVI) from different growth stages estimated by imaging were significantly correlated to drought tolerance. Under controlled growth conditions, root count at the seedling stage evaluated by imaging was significantly correlated to adult plant drought tolerance observed in the field. Random forest models were trained by integrating measurements from field and controlled conditions and revealed that plant biomass and relative growth rates at key plant growth stages are important predictors of drought tolerance. Thus, based on the results, it can be concluded that the consumer-grade cameras can be key components of affordable automated phenotyping systems to accelerate pre-breeding for drought tolerance.

Keywords

wheat; drought; machine learning; affordable phenotyping

Published in

Agronomy
2020, volume: 10, number: 6, article number: 882

Authors' information

Kumar, Dhananjay
Swedish University of Agricultural Sciences, Department of Plant Breeding
Swedish University of Agricultural Sciences, Department of Plant Breeding
Kushwaha, Sandeep (Kushwaha, Sandeep Kumar)
National Institute of Animal Biotechnology
Delvento, Chiara
Swedish University of Agricultural Sciences, Department of Plant Breeding
Liatukas, Žilvinas
Lithuanian Research Centre for Agriculture and Forestry
Vivekanand, Vivekanand
Malaviya National Institute of Technology Jaipur (MNIT)
Svensson, Jan T.
Nord Genet Resource Ctr NordGen
Henriksson, Tina
Lantmännen Lantbruk
Brazauskas, Gintaras
Lithuanian Research Centre for Agriculture and Forestry
Swedish University of Agricultural Sciences, Department of Plant Breeding

Sustainable Development Goals

SDG13 Climate action

UKÄ Subject classification

Agricultural Science

Publication Identifiers

DOI: https://doi.org/10.3390/agronomy10060882

URI (permanent link to this page)

https://res.slu.se/id/publ/106670