Abstract

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.

Keywords

barley; net blotch; disease symptoms; machine learning; RGB imaging

Published in

Plants
2024, Volume: 13, number: 7, article number: 1039

SLU Authors

• Leiva, Fernanda

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

• Ortiz Rios, Rodomiro Octavio

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

• Chawade, Aakash

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Genetics and Breeding
Agricultural Science


Publication identifier

DOI: https://doi.org/10.3390/plants13071039

Permanent link to this page (URI)

https://res.slu.se/id/publ/129141