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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.
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
URL: https://doi.org/10.1007/s10658-024-02834-z
DOI: 10.1007/s10658-024-02834-z
Data Access Statement: The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.
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