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Article

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

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Sustainable Ecosystems and Bioresources Department, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, TN, Italy
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Metacortex S.r.l., Via dei Campi 27, 38050 Torcegno, TN, Italy
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Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, TN, Italy
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Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, TN, Italy
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EFI Project Centre on Mountain Forests MOUNTFOR, Via E. Mach 1, 38098 San Michele all’Adige, TN, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Danfeng Hong
Remote Sens. 2021, 13(13), 2436; https://doi.org/10.3390/rs13132436
Received: 10 May 2021 / Revised: 18 June 2021 / Accepted: 18 June 2021 / Published: 22 June 2021
(This article belongs to the Special Issue Pattern Analysis in Remote Sensing)
Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture. View Full-Text
Keywords: agriculture 4.0; chlorophyll; early diagnosis; fungal tree pathogens; mycology; plant disease; plant pathology; smart viticulture; vegetation indices; wine grapes agriculture 4.0; chlorophyll; early diagnosis; fungal tree pathogens; mycology; plant disease; plant pathology; smart viticulture; vegetation indices; wine grapes
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MDPI and ACS Style

Calamita, F.; Imran, H.A.; Vescovo, L.; Mekhalfi, M.L.; La Porta, N. Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria. Remote Sens. 2021, 13, 2436. https://doi.org/10.3390/rs13132436

AMA Style

Calamita F, Imran HA, Vescovo L, Mekhalfi ML, La Porta N. Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria. Remote Sensing. 2021; 13(13):2436. https://doi.org/10.3390/rs13132436

Chicago/Turabian Style

Calamita, Federico, Hafiz A. Imran, Loris Vescovo, Mohamed L. Mekhalfi, and Nicola La Porta. 2021. "Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria" Remote Sensing 13, no. 13: 2436. https://doi.org/10.3390/rs13132436

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