Detection of grapevine viral diseases in Australian vineyards using remote sensing and hyperspectral technology

Grapevine viral diseases cause substantial productivity and economic losses in the Australian viticulture industry. Two economically significant grapevine viral diseases - Grapevine Leafroll Disease (GLD) and Shiraz Disease (SD) - affect numerous vineyards across major wine regions in Australia. Accurate and quick diagnosis of the virus infection would greatly assist disease management for growers. Current detection methods include visual assessment and laboratory-based tests that are expensive and labour-intensive. Low-cost and rapid alternative methods are desirable in the industry. Recent advances in low-altitude remote sensing platforms such as unmanned aerial vehicles (UAVs or “drones”) in conjunction with high-resolution multiand hyper-spectral cameras now enable large spatial-scale surveillance of plant stresses. My thesis therefore focuses on developing fast and reliable methods for GLD and SD detection on a vineyard scale using optical sensors including RGB and hyperspectral and low-altitude remote sensing technology. The thesis is constituted by a review article and three result parts, it begins with a general introduction for the background and is followed by the research goals and significance of the project that is described in Chapter 1. In order to be familiar with all possible technologies that can be potentially used for GLD and SD detection, Chapter 2 includes a comprehensive overview of methodologies for the detection of any plant viruses reviewed from laboratory-based, destructive molecular and serological assays, to state-of-the-art non-destructive methods using optical sensors and machine vision, including use of hyperspectral cameras. A key contribution of the review is that, for the first time, a detailed economic analysis or cost comparison of the various detection methodologies for plant viruses is provided. In my research, various detection methods with different degrees of complexity were attempted for GLD and SD detection. Firstly, a simple and novel detection method using the projected leaf area (PLA) calculated from UAV RGB images is proposed in Chapter 3 for the disease symptom that alters the growth of the vine such as SD in Shiraz. The PLA is closely related to the canopy size. There are significant differences in PLA between healthy and SD-infected vines in spring due to retarded growth caused by SD, which offers a simple, rapid and practical method to detect SD in Shiraz vineyards. However, for diseases that cannot be easily detected by RGB images such as GLD in the white grape cultivars, different approaches are needed. Hyperspectral technology provides a wide spectrum of light with hundreds of narrow bands compared to RGB sensors. The advanced technology can detect imperceptible spectral changes from the disease and is particularly valuable for asymptomatic disease detection. A new approach using proximal hyperspectral sensing is described in Chapter 4. Using a handheld passive (sunlight is the radiation source) hyperspectral sensor to detect GLD in the vineyard presents a simple and rapid measurement method to detect the diseases using the spectral information from the canopy. An assessment was done for the disease's spectral reflectance throughout the grape growing season for both red and white cultivars. The partial least squares-discriminant analysis (PLS-DA) was used to build a classification model to predict the disease. Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay, respectively. The proximal hyperspectral sensing technique is readily applicable to a low-altitude remote sensing method to capture high-resolution hyperspectral images for large-scale viral disease surveillance in vineyards. The subsequent study in Chapter 5 presents an advanced method to quickly detect disease using an UAV carried hyperspectral sensor. The study evaluated the feasibility of UAV-based hyperspectral sensing in the visible and near-infrared (VNIR) spectral bands to detect GLD and SD in four popular wine grapevine cultivars in Australian vineyards. The method combined the spectral and spatial analysis to classify disease for individual pixels from the hyperspectral image. The model predictions for red- and white-berried grapevine cultivars achieved accuracies of 98% and 75%, respectively. For each viral disease, unique spectral regions and optimal detection times during the growing season were identified. The spectral difference between virus-infected and healthy vines closely matched the spectral signal from the proximal sensing method in Chapter 4, which demonstrated the reliability of the low-altitude hyperspectral sensing for grapevine disease detection. Lastly, a summary of the outcomes and remaining challenges and limitations of the existing technology is discussed in Chapter 6, followed by suggestions for further research for further improvement.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food & Wine, 202

Leaf hyperspectral reflectance as a potential tool to detect diseases associated with vineyard decline.

Grape production in the Serra Gaúcha region, south of Brazil, is severily constrained by several diseases such as the decline and death syndrome caused grapevine trunk (fungal) diseases (GTDs) and the grapevine leafroll-associated virus (GLRaV). As pathogens induce changes in leaf tissue that modify the reflectance, the spectral signature of asymptomatic and symptomatic grapevine leaves infected by GTDs and GLRaV was analyzed to check whether spectral responses could be useful for disease identification. This work aims at (a) defining the spectral signature of grapevine leaves asymptomatic and symptomatic to GTDs and GLRaV; b) analyzing whether the spectral response of asymptomatic leaves can be distinguished from symptomatic; and (c) defining the most useful wavelengths for discriminating spectral responses. For such, reflectance of leaves in either condition collected in a ?Merlot? vineyard during three growing seasons was measured using a spectroradiometer. Principal components and partial least square discriminant analyses confirmed the spectral separation and classes discrimination. The average spectra, difference spectra, and first-order derivative (FOD) spectra indicated differences between asymptomatic and symptomatic leaves in the green peak (520?550 nm), chlorophyll-associated wavelengths (650?670 nm), red edge (700?720 nm), beginning of nearinfrared (800?900 nm), and shortwave infrared. Hyperspectral data was linked to biochemical and physiological changes described for GTD and GLRaV. Variable importance in the projection (VIP) analysis showed that some wavelengths allowed to differentiate the tested pathosystems and could serve as a basis for further validation and disease classification studies. Keywords Grapevine leafroll-associated virus . Grapevine trunk diseases . Vitis vinifera L. . Principal components analysis . Variable importance in the projectio

Hyperspectral imaging to assess the presence of powdery mildew (Erysiphe necator) in cv. Carignan Noir grapevine bunches

Powdery mildew is a worldwide major fungal disease for grapevine, which adversely affects both crop yield and produce quality. Disease identification is based on visible signs of a pathogen once the plant has already been infected; therefore, techniques that allow objective diagnosis of the disease are currently needed. In this study, the potential of hyperspectral imaging (HSI) technology to assess the presence of powdery mildew in grapevine bunches was evaluated. Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range). Image processing was performed to extract spectral and spatial image features and then, classification models by means of Partial Least Squares Discriminant Analysis (PLS-DA) were carried out for healthy and infected pixels distinction within grape bunches. The best discrimination was achieved for the PLS-DA model with smoothing (SM), Standard Normal Variate (SNV) and mean centering (MC) pre-processing combination, reaching an accuracy of 85.33% in the cross-validation model and a satisfactory classification and spatial location of either healthy or infected pixels in the external validation. The obtained results suggested that HSI technology combined with chemometrics could be used for the detection of powdery mildew in black grapevine bunches.This research received funding from the Department of Economic Development of the Navarre Government (Project: DECIVID (Res.104E/2017)), by the Spanish Ministry of Economy and Competitiveness (Project TIN2016-77356-P) and by the research services of the Universidad Pública de Navarra. C.P.-R is a beneficiary of postgraduate scholarships funded by Universidad Pública de Navarra (FPI-UPNA-2017 (Res.654/2017))

Potentials of Spectral Imaging for Stress Monitoring in Viticulture

Remote sensing technologies are widely used to monitor quantity and quality of plants in agriculture and forestry. While conventional panchromatic and RGB cameras can provide spatial information equivalent to human vision, high-resolution spectroscopy can reveal information about the chemical and structural composition of plants and provide detailed insight about plant health. With the development of commercial high-resolution multi- and hyperspectral cameras in the past decade, it is now possible to combine spectral and spatial information to obtain high-throughput and accurate predictions of plant condition. In this article, the underlying optical phenomena in plants will be reviewed and projected to potential imaging applications for plant stress monitoring with a focus on viticulture

Early identification of root rot disease by using hyperspectral reflectance: the case of pathosystem grapevine/Armillaria

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 viticultur

Feasibility study using remote sensing technologies to improve zonal vineyard management

The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model

Utilization of unmanned aerial vehicles and proximal sensing to detect Riesling vineyard variability

A single vineyard block can consist of significant spatial variability for several grape-growing attributes. The ability to detect and subsequently respond to this variation can lead to improved vineyard management, a growing practice termed precision viticulture. The overall goal of this research study was to determine if remote-sensing technologies could be used to detect Riesling vineyard variability, thus enhancing precision viticulture implementation. Approximately 80 grapevines in a grid pattern were geo-located within each of six commercial Riesling vineyards across the Niagara Peninsula in Ontario. From these grapevines the following variables were measured to determine their vineyard variation: soil and vine water status, vine size/vigor, winter hardiness, virus titer, yield components, and berry composition. Subsequently, remote-sensing technologies collected thermal [by unmanned aerial vehicle (UAV)] and multispectral (by UAV and ground-based proximal sensing technology GreenSeeker™) data from each block. Multispectral data were transformed into the Normalized Difference Vegetation Index (NDVI). Vineyard UAV NDVI maps were further used for selective harvesting of areas corresponding to low vs. high NDVI and wines made from these two zones were compared chemically and sensorially. The hypothesis was that remote and proximal sensing technologies could accurately detect vineyard variation for manually collected variables and further implicate differences in wine attributes upon zonal harvesting. Direct positive correlations were observed between remotely and proximally sensed NDVI vs. vine size, leaf stomatal conductance, leaf water potential, virus infection, yield, berry weight, and titratable acidity and inverse correlations with Brix and potentially-volatile terpene concentration. Maps created from remotely and proximally sensed data demonstrated similar spatial configurations to interpolated maps of these variables. In general, GreenSeeker NDVI demonstrated the most significant relationships with measured variables compared to UAV NDVI and UAV thermal data. Wines created from areas of low vs high NDVI differed inconsistently in their wine pH. Sensorially, in certain sites and vintages, panelists were able to distinguish between wines made from low vs high NDVI zones. Overall, remote sensing demonstrates the ability to detect vineyard areas differing in measures of vine health, size, yield, berry composition, and wine attributes, though more research is needed to understand the inconsistent results observed between vineyard sites and vintages

Detection of grapevine leaf stripe disease symptoms by hyperspectral sensor

Hyperspectral sensors can measure reflectance in a wide range of the electromagnetic spectrum. These can be used as an indirect method for detecting plant disease, by comparing the specific spectral signatures between symptomatic and asymptomatic vegetation. Grapevine Leaf Stripe Disease (GLSD), including the Esca complex, is a very important Grapevine Trunk Disease (GTD) worldwide. With the objective of developing an innovative method for quantitative and qualitative analyses of symptomatic plants using remote sensing, this study measured and characterized the spectral behaviour of GLSD asymptomatic and symptomatic grapevine leaves using a hyperspectral sensor. Asymptomatic, initial and final GLSD symptomatic leaves were collected in two stages of the phenological cycle (before and after harvest) from a ‘Merlot’ vineyard in Veranópolis, Rio Grande do Sul, Brazil. Reflectance measurements (350 to 2,500 nm) were performed using a spectroradiometer. The spectral behaviour of vine leaves with GLSD symptoms changed especially in the visible light range; reflectance increased in the green edge (520-550 nm) and red edge (700 nm) associated with reduced photosynthetic pigments (especially chlorophyll b). At near-infrared, reflectance decreased, especially in leaves with advanced GLSD, due to cell structure loss and toxin accumulation induced by pathogens. Even at different intensities, leaf reflectance changed in initial and final GLSD symptoms and at different stages of the cycle. These results showed that proximal, non-destructive sensing techniques may be useful tools for detecting the changing spectral behaviour of grapevine leaves with GLSD, which could be used for disease identification and detection

Detection and Diagnosis of Red Leaf Diseases of Grapes (Vitis spp) in Oklahoma

The grape industry in Oklahoma was valued at $98 million in 2010. In 2015, symptoms resembling Grapevine Leafroll disease were observed, but Grapevine Leafroll-associated Viruses were not detected using enzyme-linked immunosorbent assay (ELISA). A 2-year Cooperative Agricultural Pest Survey was initiated to determine the etiology of the red leaf symptoms in Oklahoma vineyards. In 2016, a total of 121 symptomatic grapevines from 13 counties were sampled and 96 symptomatic grapevines from 14 counties were sampled in 2017. Each sample was tested for Grapevine Red Blotch Virus (GRBV), Xylella fastidiosa (Pierce's Disease), and "Candidatus Phytoplasma spp," by polymerase chain reaction (PCR). ELISA was used to test for Grapevine Leafroll associated Virus (GLRaV) strains 1,3 and 4 strains. Rotbrenner, caused by Pseudopezicula traceiphila, (2017 only), can be found in xylem from petioles and the xylem was examined morphologically for signs of fungal structures. In 2016, GRBV was detected in 38% of 121 symptomatic samples, GLRaV-1 and -3 were detected in 16%, GLRaV 4 strains were detected in 2%, and X. fastidiosa was detected in 2%. There were no detections of"Ca Phytoplasma spp" in 2016 or 2017. In 2017, GRBV was detected in 34% of the 96 samples, GLRaV-1 and -3 were detected in 17%, GLRaV 4 strains were detected in 3%, and X. fastidiosa was detected in 3%. Rotbrenner was not detected in any of the samples in 2017. The findings of this survey provide information to Oklahoma grape growers and extension personnel about the cause of red leaf diseases affecting grapevines so that appropriate management strategies can be implemented in the near future.Entomology and Plant Patholog

Detection of "Flavescence dorée" Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed