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

Abstract

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.

The Optical Response of a Mediterranean Shrubland to Climate Change: Hyperspectral Reflectance Measurements during Spring

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A bibliometric and thematic approach to agriculture 4.0

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Analysis of RGB Plant Images to Identify Root Rot Disease in Korean Ginseng Plants Using Deep Learning

“…In our experiment, biotic stress was the only stress applied to the ginseng plants. Among the diseased plant images, the asymptotic diseased plant images [25] exist and did not show any symptoms in leaves but in roots. Figure 1 shows the plant growth conditions in the chamber.…”

“…These include stunted shoots, wilting leaves, low fruit production and dwarf leaves. Sometimes, the plants do not show any symptoms of disease (asymptomatic) in the aboveground plant region [23,25]. The roots cannot support the aboveground plant regions and they start to die.…”

“…There are many existing methods [27,28] to identify the presence of Cylindrocarpon destructans, but these methods are destructive in nature. The root rot disease has been identified from the plant leaves using hyperspectral leaf images and machine learning techniques [25]. The root images are analyzed using feature extraction methods [29] and deep learning techniques [30] to find the root rot disease in Lentil.…”