Leiva Fernanda, Dhakal Rishap, Himanen Kristiina, Ortiz Rodomiro, Chawade Aakash
Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 190, SE-23422 Lomma, Sweden.
Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, 1575 Linden Dr, Madison, WI 53706, USA.
Plants (Basel). 2024 Apr 7;13(7):1039. doi: 10.3390/plants13071039.
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 ( 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.
气候变化和人口增长的挑战因显著的环境变化而加剧，这些环境变化会扩大植物病害的范围，例如网斑病（NB），这是一种叶部病害，会显著降低大麦（L.）的籽粒产量和品质。抗性种质通常通过目视观察和病害症状评分来鉴定；然而，这是主观且耗时的。因此，自动化、无损且低成本的病害评分方法与大麦育种高度相关。本研究提出了一种评估大麦中网斑病严重程度的新型筛选方法。所提出的方法使用自动化RGB成像系统，结合机器学习，来评估网斑病的不同症状和严重程度。该研究针对三个对网斑病具有不同抗性水平（抗性、中度抗性和易感）的大麦品种进行。所测试的方法对网斑病严重程度的各类别（褪绿、坏死、真菌病斑以及叶尖坏死）显示出99%的平均精度。结果表明，所提出的方法可有效评估大麦叶片上的网斑病并确定网斑病严重程度水平；这类信息对于大麦育种中抗网斑病的精准选择可能至关重要。
