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

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.

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance

“…However, the medicinal parts of most medicinal plants, like ginsengs, are roots, whose hyperspectral reflectance data cannot be collected directly and non-constructively. Calamita et al (2021) found significant differences in the nearinfrared spectral region of leaves between healthy and root-rot grape plants by the naive bayes algorithm, which showed 90% accuracy in the identification of healthy and diseased plants, indicating the possibility for the diagnosis of root rot in plants by applying hyperspectral reflectance from leaves. However, such detection method is rarely mentioned by previous researchers and has not been reported in medicinal plants.…”

Research progress and management strategies of fungal diseases in Camellia oleifera

“…Although the initial symptoms of root rot are not obvious, subtle changes in tissue metabolic pathways occur when plant tissues are attacked by the causal fungus. The NIR broadspectrum method can rapidly identify this nuanced difference to diagnose the health status of the plant, making it effective for the early detection of plant root rot (Calamita et al, 2021). However, little research has been conducted on the differential changes in tissue metabolic pathways following infection by pathogenic fungi in C. oleifera, hindering the promotion and application of this technology in monitoring fungal diseases in this plant.…”