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Lookup NU author(s): Dr Avinash Agarwal, Filipe de Jesus Colwell, Julian Bello-Rodriguez, Dr Sarah Sommer, Professor Thomas HillORCiD, Professor Neil Boonham, Dr Ankush PrasharORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025Root rot in hydroponically grown leafy vegetables is difficult to detect via conventional manual and machine vision-based approaches as symptoms of infection are not clearly visible on the canopy at earlier stages of infection. Hence, the present study investigates the potential of using machine learning for assessing canopy information obtained from multiple imaging platforms synergistically to improve root rot detection. Herein, flat-leaf parsley seedlings were grown in an experimental hydroponic vertical farm and inoculated with Pythium irregulare and Phytophthora nicotianae. Subsequently, the seedlings were imaged via 3D, multispectral, and thermal sensors at various stages of growth to obtain twenty-six image-based plant features. Following a preliminary screening of redundant features via regression analysis, data for seventeen image features associated with morphometric, spectral, and thermal attributes was co-analyzed using supervised machine learning by Support Vector Machines (SVM). Models using all eleven spectral features provided 98 % accuracy compared to 90 % for all five morphometric features and 94 % for canopy temperature alone. Inclusion of temporal data improved model performance by ca. 0.5 %, 1.5 %, and 8 % for spectral, thermal, and morphometric datasets, respectively. Exhaustive feature selection using different SVM kernels and maximum feature thresholds showed that combining features across the three imaging platforms along with temporal information enabled better identification of infected samples (>99 %) with as low as three features in comparison to using considerably more features from individual imaging systems. Hence, fusion of data from multiple imaging systems and using it with temporal information enabled better real-time high-throughput monitoring of root rot.
Author(s): Agarwal A, de Jesus Colwell F, Bello Rodriguez J, Sommer S, Barman M, Correa Galvis VA, Hill TR, Boonham N, Prashar A
Publication type: Article
Publication status: Published
Journal: Smart Agricultural Technology
Year: 2025
Volume: 12
Print publication date: 01/12/2025
Online publication date: 23/08/2025
Acceptance date: 23/08/2025
Date deposited: 09/09/2025
ISSN (electronic): 2772-3755
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.atech.2025.101364
DOI: 10.1016/j.atech.2025.101364
Data Access Statement: Data will be made available on request.
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