Detection of Botrytis cinerea severity in rose petals using hyperspectral imaging for plant breeding applications

作者：Maikel Zerdoner, S.H.E.J. Gabriëls, Paul Arens, Richard G. F. Visser, Puneet Mishra · 发表于：Computers and Electronics in Agriculture · 年份：2025 · DOI：10.1016/j.compag.2025.110210 · 被引用次数：3 · 研究领域：Fungal Plant Pathogen Control、Plant Pathogens and Fungal Diseases、Postharvest Quality and Shelf Life Management

• Hyperspectral imaging can detect Botrytis cinerea 1 day after inoculation. • Hyperspectral imaging detects it 1 day earlier than colour imaging. • Chemometric approaches allowed visualisation of disease progression. • Disease severity can be explained with R 2 = 0.84 using near-infrared spectroscopy. Botrytis cinerea is a fungal pathogen that can affect a wide range of plants, including roses. Resistance against Botrytis is quantitative, making breeding for resistance challenging. To enable proper genetic marker development, high-throughput and objective data on Botrytis sensitivity is essential. Rose petal discs of different cultivars were manually infected with Botrytis and were monitored with hyperspectral imaging using a fully automated spectral imaging setup. Predictive modelling analysis involved both detection of Botrytis and explaining the severity of infection by linking the spectral data to visual scoring by human eye. Furthermore, band selection analysis was performed to detect key spectral bands relevant for Botrytis detection and to facilitate development of lower cost multi spectral systems for detection of Botrytis infected areas in roses. The presented approach can help plant breeders to explore and adapt to new plant phenotyping technologies such as hyperspectral imaging for breeding against biotic and abiotic stresses.