外文摘要：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.
外文关键词：machine learning;barley;disease symptoms;RGB imaging;net blotch
作者：Chawade, Aakash;Himanen, Kristiina;Ortiz, Rodomiro;Leiva, Fernanda;Dhakal, Rishap
作者单位：Univ Wisconsin;Univ Helsinki;Swedish Univ Agr Sci SLU
期刊名称：PLANTS-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：13（7）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台