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Monitoring root rot in flat-leaf parsley via machine vision by unsupervised multivariate analysis of morphometric and spectral parameters

Lookup NU author(s): Dr Avinash Agarwal, Julian Bello-Rodriguez, Dr Sarah Sommer, Professor Thomas Hill, Professor Neil Boonham, Dr Ankush Prashar



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© The Author(s) 2024.Use of vertical farms is increasing rapidly as it enables year-round crop production, made possible by fully controlled growing environments situated within supply chains. However, intensive planting and high relative humidity make such systems ideal for the proliferation of fungal pathogens. Thus, despite the use of bio-fungicides and enhanced biosecurity measures, contamination of crops does happen, leading to extensive crop loss, necessitating the use of high-throughput monitoring for early detection of infected plants. In the present study, progression of foliar symptoms caused by Pythium irregulare-induced root rot was monitored for flat-leaf parsley grown in an experimental hydroponic vertical farming setup. Structural and spectral changes in plant canopy were recorded non-invasively at regular intervals using a 3D multispectral scanner. Five morphometric and nine spectral features were selected, and different combinations of these features were subjected to multivariate data analysis via principal component analysis to identify temporal trends for early segregation of healthy and infected samples. Combining morphometric and spectral features enabled a clear distinction between healthy and diseased plants at 4–7 days post inoculation (DPI), whereas use of only morphometric or spectral features allowed this at 7–9 DPI. Minimal datasets combining the six most effective features also resulted in effective grouping of healthy and diseased plants at 4–7 DPI. This suggests that selectively combining morphometric and spectral features can enable accurate early identification of infected plants, thus creating the scope for improving high-throughput crop monitoring in vertical farms.

Publication metadata

Author(s): Agarwal A, de Jesus Colwell F, Bello Rodriguez J, Sommer S, Correa Galvis VA, Hill T, Boonham N, Prashar A

Publication type: Article

Publication status: Published

Journal: European Journal of Plant Pathology

Year: 2024

Pages: epub ahead of print

Online publication date: 19/02/2024

Acceptance date: 10/02/2024

Date deposited: 05/03/2024

ISSN (print): 0929-1873

ISSN (electronic): 1573-8469

Publisher: Institute for Ionics


DOI: 10.1007/s10658-024-02834-z


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Funder referenceFunder name
Innovate UK