Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 10 May 2021 / Approved: 11 May 2021 / Online: 11 May 2021 (14:21:25 CEST)

How to cite: 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. Preprints 2021, 2021050246 (doi: 10.20944/preprints202105.0246.v1). 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. Preprints 2021, 2021050246 (doi: 10.20944/preprints202105.0246.v1).

Abstract

The Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. The disease damage prompt assessment is crucial for pest management. However, the disease detection current methods are limited at the field scale. Therefore, an alternative approach that can enhance or supplement traditional techniques is needed. In this study, we investigated the potential of hyperspectral methods to identify the changes between fungi-infected and uninfected plants of Vitis vinifera in early detecting the Armillaria disease. The hyperspectral imaging sensor Specim-IQ was used to acquire images of leaves of the Teroldego Rotaliano grapevine cultivar. We analysed three 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 internal leaf structure changes. Asymptomatic plants emerged from the other groups due to a smaller reflectance in the red-edge spectrum (around 705nm). Hypothetically associated with the presence of secondary metabolites involved in plant defence strategy. Furthermore, significant differences were observed in the wavelengths close to 550 nm in diseased plants versus asymptomatic. We used linear discriminant analysis from a machine learning context to classify the leaves based on the most significant variables (vegetation indices and single bands), with resulting overall accuracies of 85% and 84% respectively in healthy vs. diseased and healthy vs. asymptomatic. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis on woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAV), could be a promising tool for a cost-effective, non-destructive method of Armillaria disease early diagnosis and mapping in the field, contributing to a significant step forward in precision viticulture.

Subject Areas

agriculture 4.0; chlorophyll; early diagnosis; fungal tree pathogens; mycology; plant disease; plant pathology; smart viticulture; vegetation indices; wine grapes

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