The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch

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.

Plant phenotyping relevance

オオムギ葉の病徴と網斑病重症度をRGB画像と機械学習で自動推定するスクリーニング手法を開発・評価しており、植物病害表現型の取得が中心である。

Code and data availability

The paper's RGB barley net-blotch images, TWS segmentation model, and analysis are not deposited in any public repository. The Data Availability Statement only offers the original contributions via the corresponding author, so any phenotype images or trained model would require contacting the authors. The allowed URLs,

No evidence-backed public reproduction asset is currently recorded.