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

Abstract

• 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.

Plant phenotyping relevance

バラのBotrytis感染部位と感染重症度を、完全自動化ハイパースペクトル撮像および予測モデルで検出・推定する方法が研究の中心であり、植物表現型計測法として明確に該当する。

Code and data availability

The paper's hyperspectral imaging data and analysis assets are not publicly deposited; the authors state data will be made available on request.

No evidence-backed public reproduction asset is currently recorded.