![Visual appearance of the four disease severity categories (ds1, ds2, ds3, and ds4) that are used to report the percentage of vine leaves exhibiting symptoms of Flavescence dorée (FD). The same approach is used for classification of Grapevine Trunk Disease (GTD) severity, figure retrieved from [35].](https://www.researchgate.net/publication/329900041/figure/fig3/AS:11431281211967898@1702518794503/Visual-appearance-of-the-four-disease-severity-categories-ds1-ds2-ds3-and-ds4-that_Q320.jpg)
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remote sensing
Article
On the Potentiality of UAV Multispectral Imagery
to Detect Flavescence dorée and Grapevine
Trunk Diseases
Johanna Albetis 1,† , Anne Jacquin 2,†, Michel Goulard 3,†, Hervé Poilvé 2,†, Jacques Rousseau 4,† ,
Harold Clenet 1,3,*,†,‡ , Gerard Dedieu 5,† and Sylvie Duthoit 6,†
1Ecole d’Ingénieurs de PURPAN, Université de Toulouse, Toulouse INP, 75 voie du TOEC, BP 57611,
F-31076 Toulouse CEDEX 3, France; johannaalbetis@gmail.com
2AIRBUS Defense and Space, 5 rue des satellites, F-31400 Toulouse, France; anne.jacquin@airbus.com (A.J.);
herve.poilve@airbus.com (H.P.)
3UMR 1201 DYNAFOR, Université de Toulouse, INRA, 24 chemin de borderouge, CS 52627,
F-31326 Castanet-Tolosan CEDEX, France; michel.goulard@inra.fr
4Groupe ICV, La Jasse de Maurin, F-34970 Lattes, France; jrousseau@icv.fr
5CESBIO, Université de Toulouse, UMR 5126 CNES-UPS-CNRS-IRD, 18 avenue Edouard Belin, BPI 2801,
F-31401 Toulouse CEDEX 9, France; gerard.dedieu@cesbio.cnes.fr
6TerraNIS, 12 avenue de l’Europe, F-31520 Ramonville-saint-agne, France; sylvie.duthoit@terranis.fr
*Correspondence: harold.clenet@purpan.fr; Tel.: +33-056-115-3030
† These authors contributed equally to this work.
‡ Current address: Ecole d’Ingénieurs de PURPAN, 75 voie du TOEC, BP57611,
F-31076 Toulouse CEDEX 3, France.
Received: 7 November 2018; Accepted: 18 December 2018; Published: 23 December 2018
Abstract:
Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and
Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because
of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery
could be a powerful tool for the automatic detection of symptomatic vines. However, one major
difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it
is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the
potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD
(Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2).
The study sites are localized in the Gaillac and Minervois wine production regions (south of France).
A set of seven vineyards covering five different red cultivars was studied. Field work was carried
out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199
GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired
with a MicaSense RedEdge
®
sensor and were processed to ultimately obtain surface reflectance
mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral
bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are
selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases.
Among the biophysical parameters selected, three are directly linked to the leaf pigments content
(chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of
the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine
vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve
method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in
mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold
resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the
selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion
of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels
of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars).
Remote Sens. 2019,11, 23; doi:10.3390/rs11010023 www.mdpi.com/journal/remotesensing
Remote Sens. 2019,11, 23 2 of 26
At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation
indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2.
At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2).
For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/
Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to
carotenoid content). These variables are more effective in mapping vines with a level of infection
greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the
presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors
of abnormal coloration (e.g., apoplectic vines).
Keywords:
grapevine trunk diseases; Flavescence dorée; disease detection; unmanned aerial vehicle;
vegetation indices; biophysical parameters
1. Introduction
Vine diseases have a strong impact on vineyards sustainability [
1
,
2
], which in turns leads to strong
economic consequences. Among them, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD)
are considered the two most important diseases for the European principal wine-production areas [
3
].
They are both widespread in the French vineyard, but only FD is a quarantine disease in the European
and Mediterranean Plant Protection Organization (EPPO) region (Directive 77/1993 amended 92/103)
due to its epidemic potential. FD is therefore subject to mandatory control procedures that includes the
use of pesticides and the uprooting of every infected plant [
1
]. Detection of infected plants is presently
expensive and time consuming, and, despite efforts made, FD keeps propagating in Southern Europe,
and particularly in France (58% of French vineyards concerned in 2013 against 69% in 2015 [4,5]).
Flavescence dorée is a phytoplasma–borne disease transmitted by the leafhopper Scaphoideus titanus
Ball. (Hemiptera: Cicadellidae) [
1
,
6
,
7
]. This leafhopper feeds and reproduces almost exclusively
on grapevines (Vitis vinifera L., Vitaceae) and is the only known vector of FD [
1
]. FD’s main visible
symptoms appear in the summer and remain visible until mid-autumn. They consist of drying of the
inflorescence and berries, the droop of the canes because of a lack of lignification in the new shoots,
leaves curling downwards, and leaves becoming yellowish or reddish in white and red cultivars,
respectively [
8
,
9
]. The leaves discoloration may affect only one shoot up to the whole grapevine,
and its intensity varies according to the grape variety and the year [
10
–
12
]. Grapevine Trunk Diseases
(i.e., Esca and Black Dead Arm) are caused by fungi. Symptoms can be observed at wood and foliar
levels [
13
,
14
]. Discoloration on the leaves can be similar to those observed with FD, even if its form and
texture varies. For the slow form, symptomatic leaves show mottling that consists in necrotic parts and
yellow and red spots for white and red grape cultivars, respectively. For the fast form, symptomatic
leaves totally dry out and fall prematurely [8,14–16].
Remote sensing is an efficient tool to determine crops’ health status. Stress, deficiencies,
and diseases induce changes in biophysical and biochemical characteristics of the plant tissues,
which can in turn result in changes to their optical properties [
17
–
23
]. In optical remote sensing,
even if biophysical parameters estimated using canopy reflectance modeling may also be good
candidates [
24
,
25
], the more commonly used methodology to detect those changes relies on spectral
indices calculated from multi- or hyperspectral images. When it comes to detect specific diseases,
the development of more complex indices is required because classical vegetation indices have been
proved less effective [26,27].
In the field of precision viticulture, optical remote sensing is considered as a relevant tool [
28
] and
has already been used for the detection of various diseases such as Phylloxera [
29
], grapevine leafroll
disease [
30
], Esca [
31
,
32
], and more recently Flavescence dorée [
32
–
35
]. The main remaining difficulties
in the process of detecting the Flavescence dorée disease in a robust way are (1) to discriminate the
Remote Sens. 2019,11, 23 3 of 26
disease on both white and red cultivars whatever the severity of the leaves symptoms are, and (2) to
differentiate different kinds of diseases leading to similar leaf discoloration, as it is the case with FD
and GTD for red vine cultivars [
30
,
31
,
35
]. We here propose a new methodology to address this latter
challenge using UAV imagery. This work focuses on seven red cultivars vineyards, selected over the
Gaillac and Minervois vinegrowing regions (South of France). We evaluate the potential of 24 variables
(5 spectral bands, 15 vegetation indices and 4 biophysical parameters) to separate (1) symptomatic vine
vegetation areas (FD or GTD) from asymptomatic vine vegetation areas (AS), and (2) FD symptomatic
vine areas from GTD ones. The best discriminating variables are selected for three scales of analysis
that correspond to an increasing level of operationality: by vineyard, by cultivar and by berry color.
2. Materials and Methods
2.1. Data Acquisition
Data acquisition includes field data and multispectral data acquisitions in the selected vineyards.
Those acquisitions were conducted when the disease symptoms were fully expressed, i.e., in August
2016 for the Minervois AOC area (“Appellation d’Origine Contrôlée” in French or “controlled
designation of origin” in English), and September 2016 for the Gaillac AOC area.
2.1.1. Experimental Sites
Seven vineyards within five experimental sites located in the Tarn and Herault departments in
Southwestern France have been studied: five vineyards were selected over the Gaillac AOC area
(Duras, Fer Servadou a.k.a. Braucol and Gamay cultivars) and two vineyards over the Minervois AOC
area (Grenache and Mourvèdre cultivars) (See Figure 1). For sake of clarity, vineyards will be named
further using their “vineyard ID” which is composed of cultivars’ name and eventually a letter (“A” or
“B”) when two plots of the same vine were studied. The left part of Table 1summarizes the main
characteristics of studied vineyards’ plots. In the vineyards studied, the vine training systems used
are mainly single or double guyot and cordon de Royat. Regarding the technical itinerary, pruning is
carried out during the winter (December–March). Bud-burst and flowering take place during the
spring period (March–June). Leaf thinning and veraison occur in summer (June–September) and finally
ripening and harvesting during the autumn (September–December). The vineyards are located in AOC
regions. Consequently, if the wine makers want to sell their production with this label (added-value
for their product), specific crop management practices have to be respected. It includes actions on
vine health conditions and soil quality protection. The climate of both studied regions is hot and dry,
especially in the Minervois AOC region. In the latter, the summer water deficit is important meaning
that irrigation is necessary for some vineyards.
Table 1.
Main characteristics of studied vineyards plots (site number refers to the experimental sites as
defined in Figure 1) and the total number of GPS points collected, with respect to symptomatic vines
(which include Flavescence dorée (FD), Grapevine Trunk Diseases (GTD) and others factors of abnormal
coloration (OF)) and asymptomatic vines (AS) collected for the seven selected vineyards. The classes
“ds1”, “ds2”, “ds3” and “ds4” refer to FD and GTD disease severity, expressed as the percentage of vine
leaves presenting the symptoms, and correspond to the ranges 1 to 25%, 26 to 50%, 51 to 75% and 76 to
100%, respectively.
FD GTD OF AS
ID Vineyard Vineyard
Size (ha) Site n° Orientation Total Numb.
of GPS Points ds1 ds2 ds3 ds4 ds1 ds2 ds3 ds4 Total Total
Fer_Servadou (A) 0.1 1 132° 142 19 41 33 13 1 0 3 6 3 23
Fer_Servadou (B) 0.1 2 88° 140 30 29 14 6 3 4 7 7 15 25
Gamay (A) 0.2 2 132° 183 17 38 37 23 2 3 4 13 9 37
Gamay (B) 0.5 3 165° 121 3 6 7 14 3 14 19 22 10 23
Duras 0.3 1 115° 168 17 30 15 12 3 8 10 10 24 39
Mourvèdre 0.5 4 139° 102 17 17 6 13 1 6 4 2 1 35
Grenache 0.4 5 102° 130 19 12 6 8 14 19 8 3 5 36
Remote Sens. 2019,11, 23 4 of 26
6240000 6240000
6242000 6242000
6244000 6244000
6246000 6246000
6248000 6248000
6250000 6250000
6252000 6252000
6254000 6254000
6256000 6256000
662000
662000
664000
664000
666000
666000
668000
668000
670000
670000
672000
672000
674000
674000
676000
676000
678000
678000
680000
680000
6312000 6312000
6314000 6314000
6316000 6316000
6318000 6318000
6320000 6320000
6322000 6322000
6324000 6324000
6326000 6326000
604000
604000
606000
606000
608000
608000
610000
610000
612000
612000
614000
614000
616000
616000
618000
618000
620000
620000
622000
622000
Site 1
Site 2
Site 3
Site 4 Site 5
Outline of cities
Selected sites
TARN HERAULT
Vieux
Cahuzac sur vère
Senouillac
La livinière
France
Figure 1.
Locations of the five experimental sites selected in the Tarn and Herault departments.
Maps were projected in the RGF93/Lambert-93 coordinate system.
2.1.2. Field Data Acquisition
A portable differential GPS (model Trimble Geo 7x) was used to collect over the selected vineyards
the precise location (horizontal precision of 0.01 m) of some asymptomatic vines and all vines
presenting symptoms of Flavescence dorée (FD), Grapevine trunk disease (GDT), including Esca and
Black Dead Arm (BDA), and other leaf coloration factors (OF). In this study, the severe form of Esca
known as “apoplexy” is not considered as belonging to the group of GTD vines as other factors can
also cause apoplexy (e.g., the drought). Only vines with a “tiger-stripe” aspect are considered as GTD
symptomatic vines.
For each vegetation area where symptomatic vine were observed, one GPS point was taken at
the center of the symptomatic area. Length (size of the area in vine’s row direction), thickness (size of
the area orthogonally to vine’s row direction) and disease severity were recorded (Figure 2). Disease
severity (respectively labeled FD
ds
and GTD
ds
) corresponds to the percentage of vine leaves exhibiting
the symptoms. Observations are grouped in four categories [
35
], ds1, ds2, ds3, and ds4, that correspond
to 1 to 25%, 26 to 50%, 51 to 75% and 76 to 100% of the vine presenting leaves symptoms, respectively
(Figure 3). Table 1presents the total number of GPS points corresponding to asymptomatic (AS) and
symptomatic areas (FD, GTD and OF) collected over the seven selected vineyards. Figure 4shows for
each vineyard the GPS points acquired and a picture highlighting leaves symptoms of FD and GTD
observed in the field.
Remote Sens. 2019,11, 23 5 of 26
Figure 2.
Each GPS point is located at the center of the symptomatic area where leaves’ discoloration
is observed. Recorded associated parameters are the length (size of the area in vine’s row direction),
the thickness (size of the area orthogonally to vine’s row direction) and the disease severity (described
in Figure 3).
Figure 3.
Visual appearance of the four disease severity categories (ds1, ds2, ds3, and ds4) that are
used to report the percentage of vine leaves exhibiting symptoms of Flavescence dorée (FD). The same
approach is used for classification of Grapevine Trunk Disease (GTD) severity, figure retrieved from [
35
].
2.1.3. Multispectral Data Acquisition
Multispectral images were acquired in August and September 2016 by the DELAIR company
(Toulouse, France) using a DT-5Bands imaging instrument on-board a long range DT-18 UAV platform.
The DT-5Bands camera is based on the MicaSense RedEdge
®
sensor and acquires images at five
wavelengths: Blue (455–495 nm), Green (540–580 nm), Red (658–678 nm), Red-Edge (707–727 nm),
and Near Infrared (800–880 nm). Characteristics of UAV and sensor are presented in Table 2.
The MicaSense RedEdge
®
sensor data were converted to reflectance based on the measurement
in the field of a Spectralon reference surface. Image radiance data were divided by the radiance
measured on the reference surface and multiplied by the known, calibrated reflectance of this surface.
This pre-processing was performed by the UAV operator (DELAIR Company). The Pix4D software
(Available at: https://pix4d.com/) was used to orthorectify, mosaic and resample at 0.10 m spatial
resolution the UAV images. In the final product, pixel values correspond to surface reflectance in each
spectral band (see Figure 4).
Remote Sens. 2019,11, 23 6 of 26
Figure 4.
Localization of the collected GPS points over the seven selected vineyards and their associated
disease level (point size increases as a function of disease severity). Typical examples of abnormal
discolorations (Flavescence dorée and Grapevine Trunk Disease) observed for each plots are reported
in the top right of each subset. RGB colored-composition from Unmanned Aerial Vehicle (UAV)
multispectral images are used as background.
Table 2.
Unmanned Aerial Vehicle (UAV) platform and sensor characteristics used for the multispectral
data acquisition.
Characteristic Name Description
Platform Long range DT-18
Sensor DT-5Bands
Number of bands 5
Spectral wavelengths
Blue (455–495 nm)
Green (540–580 nm)
Red (658–678 nm)
Red-edge (707–727 nm)
NIR (800–880 nm)
Focal length 5.5 mm
Remote Sens. 2019,11, 23 7 of 26
Table 2. Cont.
Characteristic Name Description
Field of view 47.2°
Flight altitude Above Ground Level (AGL) 150 m
Ground resolution 0.08 m/pixel
2.2. Data Processing and Analysis
Figure 5shows the methodological framework’s main steps used in this study. Statistical analyses
are conducted at three scales: by vineyard (five vineyards), by cultivar when possible (two vineyards
by cultivar for Fer Servadou and Gamay) and by berry color (all vineyards). For each scale, two steps
associated with the two discrimination’s cases are considered:
- Case 1: identification of the best variables (spectral bands, vegetation indices and/or biophysical
parameters) to discriminate FD and GTD symptomatic vine vegetation from asymptomatic vine
vegetation. This first case allows us to investigate if detection of vine diseases is possible.
- Case 2: identification of the best variables to discriminate FD from GTD symptomatic vine
vegetation (from vines identified as symptomatic in case 1). This case allows us to determine if FD
symptoms can be specifically detected.
For each scale and for each case, the same methodology is applied to test each variable (see Tables 3
and 4): selection of the best variable(s), mapping of symptomatic vine vegetation with the selected
variable(s) and validation using field data.
Figure 5.
Methodological framework with the main steps of data acquisition, processing and analysis.
Class A and Class B vary according to the case of discrimination. For example, when the potential
of multispectral variables for the discrimination of symptomatic vine vegetation (FD or GTD) from
asymptotic vine vegetation (AS) is evaluated (Case 1), class A corresponds to symptomatic vine
vegetation and class B to asymptomatic vine vegetation.
Remote Sens. 2019,11, 23 8 of 26
2.2.1. Computing Vegetation Indices and Biophysical Parameters
Selected vegetation indices (VI) and biophysical parameters (BP) are computed from the UAV
images’ spectral bands (SB). Table 3gives the name, formula, biophysical indicator, and associated
reference(s) for each vegetation indices. Most of the vegetation indices were found in literature
on detecting plant diseases and changes in the pigment content of plants. Two vegetation indices
(GRVI and NDRE) are based on the results obtained with hyperspectral database providing leaf
reflectance measurements collected on FD and asymptomatic vine leaves during 2015 in the plots of
Gaillac AOC area [
36
]. At this scale, GRVI is the best vegetation index to discriminate symptomatic
leaves from asymptomatic ones for red cultivars and NDRE for white cultivars. Table 4gives the
name, the description, the unit and the typical range of the four selected biophysical parameters.
The biophysical parameters are generated by inverting a canopy reflectance model based on the
coupling of the PROSPECT-D leaf [
37
,
38
] and the SAIL canopy model [
39
] using the Overland software
developed by AIRBUS DS Geo-Intelligence (Toulouse, France) [
37
]. The possibility with the Overland
software to use the new model PROSPECT-D [
38
,
40
], which includes the three main families of leaf
pigments (chlorophylls, carotenoids, and anthocyanins) as independent constituents, is particularly
interesting for our study as we know that leaves’ discolorations observed in FD symptomatic vines are
linked to a change in leaves’ pigment concentration, particularly in anthocyanins for red cultivars [
41
].
The full calculation method is described in [
35
]. The performance of the biophysical information that
is retrieved with the Overland software actually depends on the radiometric performance, calibration
and spectral band set of the sensor. The radiometric performance of MicaSense RedEdge
®
sensor
data is good (low noise level) and gain accuracy can reach 3–5% if properly calibrated. According to
Overland developers and considering the five spectral bands (including red-edge) of the MicaSense
RedEdge
®
sensor, sensitivity (i.e., precision) is good for chlorophylls and anthocyanins (precision of
2–3
µ
g/cm
2
) and moderate for carotenoids; accuracy is more variable as systematic bias could appear
in this UAV application due to sensor radiometric calibration and canopy heterogeneity at such high
spatial resolution.
Table 3.
Vegetation indices selected to discriminate FD and/or GTD symptomatic vines. For each
variable, we show the formula, the biophysical indicator and the references.
Index Name Formula Biophysical Indicator References
- Normalized Difference
Vegetation Index (NDVI) (NIR −Red)/(NIR + Red)
Biomass
[31,42]
- Green-Red Vegetation
Index (GRVI) (Green + Red)/(Green + Red) [36,43]
- Green Normalized Difference
Vegetation Index (GNDVI) (NIR −Green)/(NIR + Green) [44,45]
- Difference Vegetation
Index (DVI) NIR −Red [46,47]
- Soil Adjusted Vegetation
Index (SAVI)
(NIR −Red) ×(1 + L)/(NIR + Red + L)
L = 0.5 [19,48]
- Anthocyanin Reflectance
Index (ARI) Green−1−RedEdge−1
Anthocyanins
Content
[24,49]
- Modified Anthocyanin
Reflectance Index (MARI) (Green−1−RedEdge−1)×NIR [24,50]
- Red Green Index
(RGI) Red/Green [25]
- Modified Anthocyanin
Content Index (MACI) NIR/Green [24,51]
- Anthocyanin Content
Index (ACI) Green/NIR [52]
- Chlorophyll Index
(CI) (NIR/RedEdge) −1
Chlorophyll
content
[43,53]
- Normalized Pigment
Chlorophyll Index (NPCI) (RedEdge −Blue)/(RedEdge + Blue) [51,54,55]
Remote Sens. 2019,11, 23 9 of 26
Table 3. Cont.
Index Name Formula Biophysical Indicator References
- Normalized Difference
Red-edge Index (NDRE) (NIR −RedEdge)/(NIR + RedEdge) [36,51]
- Red-edge Green
Index (REGI) (RedEdge −Green)/(RedEdge + Green)
Not defined
-
- Red-edge Rouge
Index (RERI) (RedEdge −Red)/(RedEdge + Red) -
Table 4. Biophysical parameters estimated with the Overland software.
Parameter Name Acronym Description Unit and Typical Range
fCover fCover Fractional cover of green vegetation
(interception in vertical view) 0.0 to 1.0
Leaf Anthocyanin content Ant Anthocyanin content in the leaves
(per leaf unit area) 0 to 12 µg/cm2
Leaf Carotenoid content Car Carotenoid content of the leaves
(per leaf unit area) 0 to 15 µg/cm2
Leaf Chlorophyll content Chl Chlorophyll content in the leaves
(per leaf unit area) 20 to 80 µg/cm2
2.2.2. Extraction of Symptomatic and Asymptomatic Vine Vegetation Areas
The classification used to create the mask of vine vegetation has been realized with the Orfeo
ToolBox [
56
] developed by the French National Space Agency. For this study, we train an SVM (Support
vector Machine) model using a radial basis function (RBF) kernel classification [
57
] with three or four
output classes, from 1000 randomly generated training samples, each of them having five components
(five spectral bands). For AOC gaillac vineyards (Fer Servadou (A), Fer servadou (B), Gamay (A),
Gamay (B) and Duras), four classes are used: 1—bare soil, 2—shadow, 3—inter-row vegetation and
4—grapevine vegetation. In the case of AOC Minervois vineyards (Mourvèdre and Grenache), only
three classes are used: 1—bare soil, 2—shadow, and 3—grapevine vegetation. The classification is
applied for each vineyard separately. The accuracy metric [
58
] for each vineyard is shown in Table 5.
Best results are obtained in the vineyards of Mourvèdre, Grenache and Gamay (B). From the export of
class grapevine vegetation, a mask of vine vegetation is created
Table 5.
Accuracy metric obtained with the Support Vector Machine (SVM) classification applied on
the multispectral imagery acquired by Unmanned Aerial Vehicle (UAV).
Vineyard Accuracy Metric (%)
Fer_Servadou (A) 90.13
Fer_Servadou (B) 95.87
Gamay (A) 98.43
Gamay (B) 99.02
Duras 92.53
Mourvèdre 99.64
Grenache 99.51
From this mask, the pixel values of the 5 SB, 15 VI and 4 BP located within (FD or GTD) and
asymptomatic (AS) areas were extracted (Figure 5) as well as the pixel type (AS, FD or GTD) and a
categorical variable representing the disease severity observed in the field (ds1, ds2, ds3 and ds4).
Buffers corresponding to symptomatic and asymptomatic vine vegetation areas were created from the
GPS points and from the width and length of the vine vegetation collected in field. The number of
vine vegetation areas and the total number of pixels finally used for the statistical analysis (training
and validation) are presented in the Table 6.
Remote Sens. 2019,11, 23 10 of 26
Table 6.
Number of vine vegetation (VV) areas and corresponding number of valid pixels (VP) (between brackets) used for the selection of the best variables and
mapping of symptomatic vines (training dataset) and the validation steps (validation dataset) for each scale of analysis (vineyard, cultivar and berry color) for Case 1
(discrimination of asymptomatic AS and symptomatic FD or GTD) and Case 2 (discrimination of symptomatic FD and symptomatic GTD). DS1, DS2, DS3 and DS4
corresponds to the different levels of disease severity observed in the field.
Study Case Case 1 Case 2
Statical Analysis Training Validation Training Validation
Disease Severity AS FD or GTD AS FD or GTD FD GTD FD GTD
- DS4 - DS1 DS2 DS3 DS4 DS4 DS4 DS1 DS2 DS3 DS4 DS1 DS2 DS3 DS4
Vineyard
Fer_Servadou (A) VV 14 14 9 20 41 36 5 4 4 19 41 33 9 1 -3 2
(VP) (285) (634) (184) (819) (1917) (1813) (254) (196) (228) (762) (1917) (1715) (323) (57) (98) (141)
Fer_Servadou (B) VV 9 9 16 33 33 21 4 4 4 30 29 14 2 3 4 7 3
(VP) (295) (340) (503) (934) (1187) (723) (164) (81) (181) (828) (1011) (410) (97) (106) (176) (313) (145)
Gamay (A) VV 28 28 9 19 41 41 8 10 10 17 38 37 13 2 3 4 3
(VP) (608) (1003) (201) (583) (1481) (1468) (336) (409) (372) (484) (1393) (1262) (449) (99) (88) (206) (109)
Gamay (B) VV 18 18 5 6 20 26 18 11 11 3 6 7 3 3 14 19 11
(VP) (291) (710) (78) (149) (777) (867) (504) (443) (405) (78) (146) (181) (81) (71) (631) (686) (285)
Duras VV 17 17 22 20 38 25 5 8 8 17 30 15 4 3 8 10 2
(VP) (355) (539) (421) (542) (1224) (787) (126) (228) (291) (466) (1026) (410) (103) (76) (198) (377) (43)
Cultivar
Fer_Servadou VV 25 25 23 53 74 57 7 10 10 49 70 47 9 4 4 10 3
(VP) (635) (1117) (632) (1753) (3104) (2536) (275) (387) (513) (1590) (2928) (2125) (310) (163) (176) (411) (182)
Gamay VV 57 57 3 25 61 67 15 28 28 20 44 44 9 5 17 23 7
(VP) (1124) (1955) (54) (732) (2258) (2335) (598) (996) (944) (562) (1539) (1443) (386) (170) (719) (892) (227)
Berry color
Red VV 121 121 97 149 227 173 31 50 50 122 173 118 39 27 54 55 13
(VP) (2460) (4488) (1930) (4448) (8203) (6438) (1130) (1757) (1844) (3603) (6402) (4437) (1535) (845) (1801) (2001) (482)
Remote Sens. 2019,11, 23 11 of 26
2.2.3. Statistical Analysis
The initial dataset of vine vegetation areas is split in two parts: one part for the selection of the best
variables and mapping of symptomatic vines (training dataset) and the other part for the validation
(validation dataset).
For case 1, the training dataset contains 80% of symptomatic areas (FD or GTD) with the most
severe symptoms (FD
ds4
/GTD
ds4
) and the same number of asymptomatic areas. The validation dataset
corresponds to the vine vegetation areas belonging to the three other disease severity categories
of FD and GTD (ds1, ds2 et ds3) and the remaining asymptomatic and symptomatic areas (20% of
FDds4/GTDds4).
For case 2, the dataset is composed only by symptomatic areas. The training dataset contains
80% of FD
ds4
symptomatic areas and the same number of GTD
ds4
symptomatic areas. The validation
dataset consists of the remaining symptomatic areas (FD or GTD). Table 6provides the total number of
vines and pixels corresponding to the dataset used for statistical analysis (training and validation).
Equally, this dataset is used for the three scales of analysis (vineyard, cultivar and berry color) and for
two cases of discrimination (Case 1 and Case 2).
Step 1: Selection of the best discriminating variables
The objectives of the statistical analyses carried out is to select the SB or VI and BP that present
the best discrimination potential. This analysis was realized using the samples available for each scale
of analysis (as shown in Table 6), based on the Receiver Operator Characteristic (ROC) analysis [
59
–
61
].
The capacity of each selected variable to discriminate symptomatic (FD or GTD) from asymptomatic
areas (Case 1) and FD from GTD symptomatic areas (Case 2) is evaluated. Table 7provides an
example of the interpretation of potential classification outcomes for Case 1. Three indicators of
classification performance derived from the ROC analysis are used: the sensitivity (Se = TP/TP + FN),
the specificity (Sp = TN/FP + TN) and the Area Under Curve (AUC). We define an optimal threshold
that corresponds to the maximum of the Youden index (Se + Sp
−
1) and allows for minimizing errors
on both sensibility and specificity values [
59
]. AUC ranges from 0.0 to 1.0 and a random classifier
presents an AUC of 0.5 (high amount of errors). Applied to the training dataset, experiments are
performed using a random repeated hold-out strategy. The number of repetitions is 200. For each
run, 2/3 of the training dataset are used for calibration and 1/3 for testing the classification accuracy
(test) [
62
]. All selected variables with an AUC under 0.7 are considered as non-performing and then
eliminated. The remaining variables are first ordered according to the mean AUC value, then to the
mean sensitivity value, and then to the mean specificity value. The comparison between the means
of these parameters (AUC, sensibility and specificity) for each variable is realized using the Welch’s
t-test [63].
Table 7. Interpretation of potential classification outcomes for Case 1.
Reference Data
Symptomatic (FD or GTD) Asymptomatic
Classification results
Symptomatic
(FD or GTD)
True positive (TP)
(Symptomatic pixel classified as Symptomatic)
False positive (FP)
(Asymptomatic pixel classified as Symptomatic)
asymptomatic False negative (FP)
(Symptomatic pixel classified as Asymptomatic)
True negative (TN)
(Asymptomatic pixel classified as Asymptomatic)
Step 2: Mapping of symptomatic vine vegetation at the whole vineyard scale
This mapping is carried out by applying an optimal threshold on the selected variables (binary
approach). The threshold values are obtained using the ROC method from the training datasets
(see Table 6) available for each scale of analysis (vineyard, cultivar and berry color) and the two
discrimination cases. For Case 1, the optimal threshold is applied to the entire vineyard. A binary
map showing predicted symptomatic (FD or GTD) and asymptomatic pixels for each selected variable
is obtained. For Case 2, the optimal threshold is applied to the symptomatic areas previously
Remote Sens. 2019,11, 23 12 of 26
detected with Case 1. This allows for creating a binary map showing predicted symptomatic FD
and symptomatic GTD pixels.
Step 3: Validation
The objectives are first to appreciate if the mapping established in the step before could allow
the detection of symptoms even inside areas where they are more diffuse and then if it is sensitive
enough to characterize the level of infection of vine vegetation areas. This work was realized using
the validation dataset available for each scale of analysis (as shown in Table 6). Validation samples
are mainly composed of FD or GTD vegetation areas with disease levels of less than 75%. Inside each
of these validation samples, the model was applied. A rate of predicted symptomatic pixels (FD or
GTD) with respect to the total number of pixels was calculated from the mapping of symptomatic
vines. This rate corresponds to the classifier-predicted disease severity. For the three scales of analysis
(vineyard, cultivar and berry color), classifier-predicted disease severity was compared to disease
severity observed in the field for Cases 1 and 2. In order to get comparable information between field
disease severity and classifier-predicted disease severity, rates were first converted into five discrete
classes (0, 1, 2, 3 or 4), each corresponding to one of the four disease severity (0%, 1–25%, 26–50%,
51–75% and 75–100%) classes. For Case 1, class 0 corresponds to asymptomatic areas and, for Case
2, to GTD symptomatic areas. For this last case, asymptomatic pixels were excluded and only the
four disease severity of FD classes were considered. Considering that those classes are ordinal and
equidistant, the validation was handled as a regression problem. The overall agreement between field
and predicted classes for a given vineyard was verified through the root-mean-squared error (RMSE)
as formalized in Equation
(1)
. Values of 1 or 3 respectively mean a difference of 1 or 3 FD
ds
classes
between field and predicted classes:
RMSE =
v
u
u
t∑ClassValuepredicted −Cl assValuefield2
n. (1)
3. Results
3.1. Selection of the Best Discriminating Variables
Tables 8and 9present the best VI and BP and corresponding mean performance parameters
(AUC, sensibility and specificity) for Case 1 and Case 2 at the three scales of analysis considered in
this work (vineyard, cultivar and berry color). Figure 6illustrates the distribution of symptomatic and
asymptomatic pixels values for one of the best discriminating variables. Based on the comparison
of performance parameters’ values, we can first note that performances of selected variables (SB,
VI and BP) globally decrease with the scales of analysis (from vineyard to berry color scale) and that
variables are in general more efficient for Case 1. This is easily understandable when looking at Figure 6
where we observe—particularly for BP—a higher cover-up for variables selected for Case 2, leading to
a global higher confusion between FD symptomatic and GTD symptomatic pixels. Consequently,
best AUC values, whatever the variable selected, reach at maximum 0.95, 0.89 and 0.84 for Case 1 at
vineyard, cultivar and berry color scale, respectively. For Case 2, best AUC values reach 0.90 and 0.79 at
vineyard and cultivar scales, respectively. No variables show satisfactory discriminating performance
at the berry color scale (AUC < 0.7). Associated sensibility and specificity values are generally lower
than for Case 1, meaning that commission and/or omission errors are even more frequent. The best
discriminating VI and BP parameters are not necessarily the same according to the case and the scale
studied. Next, results are analyzed for the two interesting categories of variables, vegetation indices
and biophysical parameters.
Remote Sens. 2019,11, 23 13 of 26
3.1.1. Best Vegetation Indices
For Case 1
(Table 8), RGI and GRVI (both based on green and red spectral bands) or CI and
NDRE (both based on RedEdge and NIR) are globally found as the best discriminating vegetation
indices at the three considered scales (except for the Fer_Servadou (A) vineyard) with quite good
performances. At berry color scale, RGI and GRVI are the best variables. They show both the same
poor discrimination performances with a mean AUC equal to 0.77, which is close to a random classifier.
The sensitivity of 0.77 obtained for these two indices implies that 23% of the pixels located within
the symptomatic areas showing a high level of infection (75–100%) were classified as asymptomatic
pixels. The sensitivity of 0.66 implies that 34% of the pixels located within the asymptomatic areas are
classified as symptomatic pixels.
In Case 2
(Table 9), RERI and REGI are the best vegetation indices at vineyard and cultivar scale
(except for the Fer_Servadou (B) and Gamay (A) vineyards). However, performances are always very
poor (AUC < 0.8) at the cultivar scale. For example, when applying the REGI variable to the Gamay
plots at cultivar scale, the sensitivity of 0.56 and the specificity of 0.94 indicate that: 44% of the pixels
within the FD symptomatic areas are classified as GTD symptomatic pixels, and conversely 6% of the
pixels located within the GTD symptomatic areas are classified as FD symptomatic pixels. At berry
color scale, no vegetation index presents satisfying AUC values (up to 0.7).
Table 8.
Two best biophysical parameters and vegetation indices to discriminate symptomatic vines
vegetation areas (Flavescence dorée or Grapevine Trunk disease) from asymptomatic vines vegetation
areas at three scale of analysis (by vineyard, by cultivar and by berry color. When two vegetation
indices appear on the same line, their discrimination performances are equal).
Study Case Case 1—FD or GTD and Asymptomatic Vines
scale of Analysis Best VI or BS Best BP
Position Variable (AUC, Sens./Spe.) Variable (AUC, Sens./Spe.)
Vineyard
Fer Servadou (A) 1 REGI (0.92 , 0.83/0.87) Car (0.93, 0.84/0.91)
2 GRVI (0.90, 0.82/0.82) AUC < 0.70 -
Fer Servadou (B) 1RGI (0.94, 0.85/0.85 ) fCover (0.89, 0.7/0.92)
2 GRVI (0.94, 0.84/0.87) AUC < 0.70 -
Gamay (A) 1 CI, NDRE (0.89, 0.85/0.75) Car (0.90, 084/0.82)
2 NDVI (0.80, 0.89/0.63) fCover (0.82, 0.87/0.70)
Gamay (B) 1 RGI, GRVI (0.86, 0.81/0.74) Car (0.90, 0.82/0.79)
2 CI, NDRE (0.81, 0.64/0.76) fCover (0.86, 0.85/0.75)
Duras 1GRVI (0.95, 0.93/0.85 ) Car (0.92, 0.81/0.89)
2 RGI (0.95, 0.92/0.85) fCover (0.91, 0.84/0.81)
Cultivar
Fer Servadou 1RGI, GRVI (0.89, 0.81/0.81) Car (0.87, 0.75/0.94 )
2 REGI (0.88, 0.73/0.89) fCover (0.86, 0.75/0.84)
Gamay 1CI, NDRE (0.88, 0.77/0.8) Car (0.84, 0.71, 0.84)
2 NDVI (0.80, 0.84/0.64) fCover (0.78, 0.72/0.74)
Berry color
Red 1 GRVI, RGI (0.77, 0.77/0.66) Car (0.84, 0.75/0.80)
2 CI, NDRE (0.71, 0.80/0.55) fCover (0.74, 0.78/0.62)
Remote Sens. 2019,11, 23 14 of 26
Table 9.
Two best biophysical parameters and vegetation indices to discriminate Flavescence dorée
symptomatic vines vegetation areas from Grapevine Trunk Disease symptomatic vine vegetation areas
at three scale of analysis (vineyard, cultivar and berry color).
Study Case Case 2—FD and GTD Vines
Scale of Analysis Best VI or SB Best BP
Position Variable (AUC, Sens./Spe.) Variable (AUC, Sens./Spe.)
Vineyard
Fer Servadou (A) 1RERI (0.84, 0.76/0.74) fCover (0.79, 0.76/0.62)
2 GRVI (0.78, 0.69/0.69) Car (0.78, 0.67/0.72)
Fer Servadou (B) 1AUC < 0.70 - Ant (0.77, 0.64/0.82)
2 AUC < 0.70 -
Gamay (A) 1 ARI (0.85, 0.79/0.76) AUC < 0.70 -
2 RED (0.80, 0.71/0.79)
Gamay (B) 1 REGI (0.90, 0.77/0.9) Car (0.77, 0.67/0.79)
2 ARI (0.81, 0.69/0.81) AUC < 0.70 -
Duras 1 REGI (0.84, 0.76/0.80) AUC < 0.70 -
2 MARI (0.81, 0.72/0.75)
Cultivar
Fer Servadou 1RERI (0.79, 0.76/0.67) Car (0.74, 0.72/0.70)
2 RED (0.75, 0.79/0.55) AUC < 0.70 -
Gamay 1REGI (0.78, 0.56/0.94) AUC < 0.70 -
2 MARI (0.72, 0.5/0.80) AUC < 0.70 -
Berry color
Red 1AUC < 0.70 - AUC < 0.70 -
2
3.1.2. Best Biophysical Parameters
For Case 1
, whatever the scale of analysis considered (vineyard, variety or berry color), the BP Car
(related to leaf carotenoid content) is the most often selected as the best biophysical parameter with
mean AUC values between 0.84 and 0.93. Car values are always higher for symptomatic areas than for
asymptomatic ones with a global low covering up between the datasets (Figure 6). At the berry color
scale, performances are even better with the Car parameter (AUC 0.84, Sensibility/Specificity 0.75/0.8)
than with the selected GRVI/RGI indices.
For Case 2
, whatever the scale of analysis considered,
overall performances are not satisfying. AUC values are below or close to 0.7. Results do not highlight
a common biophysical parameter that can be used either at vineyard, variety or berry color scales.
3.2. Validation and Mapping at the Vineyard Scale
Figure 7presents, for the three scales of analysis and the two discrimination cases, the comparison
between the infection level predicted by the best variables (i.e., with the lowest RMSE) against the
infection level observed in field. RMSE values globally indicate a difference of around one disease
severity class, with RMSE values ranging from 0.63 to 1.18. Figure 7shows a slight increase in RMSE
for Case 2 compared to Case 1. Differences of performances are observed between vineyards, although
similar results are obtained regarding vineyard scale and berry color scale. For example, in the case
of symptomatic areas for Case 1 with a level of infection of 1–25%, the RMSE close to 1 means that
symptomatic areas (FD or GTD) with an infection level of 1–25% (Class 2) are often classified as
symptomatic areas with an infection level of 25–50% (Class 3) or as asymptomatic areas (Class 1).
In Case 2, all GTD symptomatic areas were grouped into Class 1. The RMSE close to 1 indicates that
FD symptomatic areas with an infection level of 1–25% (Class 2) are often classified as FD symptomatic
areas with an infection level of 25–50% (Class 3) or as GTD symptomatic areas (whatever the level
of infection).
Remote Sens. 2019,11, 23 15 of 26
Figure 6.
Boxplot of one best vegetation index and biophysical parameter per case and scale of analysis
to separate symptomatic (FD or GTD) and asymptomatic vines (Case 1); and FD and GTD symptomatic
vines (Case 2). For each variable, we show the optimal threshold (Red line).
The validation step is completed by visually analyzing the maps realized with the best selected
variables at the whole vineyard scale. Figure 8shows, for the Gamay (B) vineyard and each scale
of analysis, an example of the mapping of predicted symptomatic vines (Case 1, FD or GTD) and
Remote Sens. 2019,11, 23 16 of 26
predicted FD or GTD for the Case 2. No mapping has been performed for the Case 2 at the berry color
scale because no variables have shown satisfying discrimination performances. Figures A1–A3 show
the mapping for all vineyards and all scales of analysis.
0
0,2
0,4
0,6
0,8
1
1,2
1,4
RERI Ant ARI REGI REGI RERI REGI
Car RGI CI Car Car Car Car Car
Fer_Servadou (A) Fer_Servadou (B) Gamay (A) Gamay (B) Duras Fer Servadou Gamay Red
VINEYARD CULTIVAR BERRY COLOR
RMSE - Case 1 RMSE - Case 2
Case 2
Case 1
ID
Scale
Figure 7.
Validation results for Case 1 (discrimination between symptomatic FD or GTD/asymptomatic)
and Case 2 (discrimination between symptomatic GTD and symptomatic FD) and each scale of analysis
(vineyard, cultivar and berry color). RMSE: root-mean-square error.
In Case 1 (left part of Figure 8) and for the Gamay (B) vineyard taken as an example, the maps
of predicted symptomatic areas obtained using the Car variable are very similar for the three scales
of analysis because the three optimal thresholds are very close: 5.59, 5.65 and 5.67 for the vineyard,
cultivar and berry color scales, respectively (Figure 6). Whatever the level of analysis, we observe
a lot of commission errors (false FD or GTD predicted pixels) over areas where no symptoms have
been observed in the field. Figure 9illustrates the main situations causing misclassification of false
symptomatic pixels. Table 10 shows the percentage of each of the situation observed over the whole
validation dataset used in the study for each vineyard.
Table 10.
Percentage of pixels for each of the main misclassification situations for Case 1 for
each vineyard using the biophysical parameter Car calibrated by berry color (Optimal threshold
= 5.62).
ID Vineyard Random Pixels (%) Edges of Vines Rows (%) Drop Shadow (%) Absence (%) Total AS Vines
Fer_Servadou (A) 36.36 54.55 0.00 9.09 11
Fer_servadou (B) 0.00 63.64 9.09 27.27 11
Gamay (A) 25.00 56.25 0.00 18.75 16
Gamay (B) 0.00 12.50 0.00 87.50 8
Duras 6.25 43.75 6.25 43.75 16
Mourvedre 0.00 69.23 0.00 30.77 13
Grenache 80.00 0.00 0.00 20.00 20
For five vineyards over the seven studied, the first situation of commission errors concerns the area
along the vine rows where mixed soil/vegetation (Edges of the rows of vines) or shadow/vegetation
(Drop shadow) pixels are observed. Commission errors also occur in the middle of the vine rows
where pixels can be grouped or dispersed (random pixels). Some omission errors are also visible. It is
interesting to notice that the vegetation areas with disease verity inferior to 50% (DS1 and DS2) are
more difficult to detect and are often classified as asymptomatic vine vegetation. As it can be seen in
Figure 3, sometimes the leaves affected by the discoloration are located on the side of the vine row and
are consequently not visible on the UAV images for the lower infection levels. This fact could explain
Remote Sens. 2019,11, 23 17 of 26
the confusion among asymptomatic and DS1/DS2 classes. In the higher infection levels, there are so
many discolored leaves that many of them are visible in the upper part of the vine canopy.
DS4
DS4
DS4
DS4
DS4
DS3
DS4
DS4
DS3
DS2
DS1
DS3
DS4
DS2
DS4
DS3
DS3
DS3
DS4
DS4
DS4
DS4
DS4
DS3
DS4
DS4
DS3
DS2
DS1
DS3
DS4
DS2
DS4
DS3
DS3
DS3
DS4
DS4
DS4
DS4
DS4
DS3
DS4
DS4
DS3
DS2
DS1
DS3
DS4
DS3
DS2
DS4
DS3
DS3
DS4
DS4
DS4
DS4
DS4
DS3
DS4
DS4
DS3
DS2
DS1
DS3
DS4
DS2
DS4
DS3
DS3
DS3
DS4
DS4
DS4
DS4
DS4
DS3
DS4
DS4
DS3
DS2
DS1
DS3
DS4
DS2
DS3
DS4
DS3
DS3
DS4
DS4
DS4
DS4
DS4
DS3
DS4
DS4
DS3
DS2
DS1
DS3
DS4
DS3
DS2
DS4
DS3
DS3
DS4
DS4
DS4
DS4
DS4
DS3
DS4
DS4
DS3
DS2
DS1
DS3
DS4
DS2
DS4
DS3
DS3
DS3
0 20 40 6010
Meters
0 21
Meters
0 20 40 6010
Meters
0 21
Meters
0 20 40 6010
Meters
0 20 40 6010
Meters
0 20 40 6010
Meters
0 20 40 6010
Meters
0 20 40 6010
Meters
0 21
Meters
0 21
Meters
0 21
Meters
0 21
Meters
0 21
Meters
Grapevine Trunk Disease vines (ESCA and BDA)
Others leaves coloration factors
Asymptomatic vines
FD : Flavescence dorée pixels
GTD : Grapevine Trunk Disease
AS : Asymptomatic
DS : Disease Severity
Flavescence Dorée vines
Level by vineyard
Car
CI ARI
ARI
Car REGI
Level by cultivar
Level by color
FD or GTD
AS
FD
GTD
FD
GTD
FD
GTD
Case 1 - (FD or GTD) and asymptomatic vines Case 2 - FD and GTD symptomatic vines
FD or GTD
AS
FD or GTD
AS
FD or GTD
AS
Car
Figure 8.
Map disease using the best variable (vegetation index or biophysical parameters) calibrated
by vineyard, cultivar and color to discriminate symptomatic vine vegetation (red pixels) from
asymptomatic vine vegetation (green pixels) (Case1); and Flavescence dorée (FD) symptomatic vine
vegetation (red pixels) from Grapevine Trunk Disease (GTD) symptomatic vine vegetation (blue pixels)
(Case 2). The rectangles correspond to the FD (Black), GTD (yelow), other leaves’ coloration factors
(grey) symptomatic vines and asymptomatic vines (green) located in the field; the example with the
Gamay (B) field (red cultivar).
Remote Sens. 2019,11, 23 18 of 26
0 40 8020
Centimeters
0 40 8020
Centimeters
0 40 8020
Centimeters
0 40 8020
Centimeters
0 40 8020
Centimeters 0 40 8020
Centimeters
0 40 8020
Centimeters
0 40 8020
Centimeters
asymptomatic pixels
symptomatic pixels
(FD or GTD)
asymptomatic areas
Level of analyse : Berry color
Car PB - OT= 5.62
Random pixels Edges of vines rows Drop shadow Absence
Grenache Mourvedre Fer_Servadou (B) Gamay (B)
Figure 9.
Description of classification errors within asymptomatic areas using the biophysical parameter
Car calibrated by berry color (Optimal threshold = 5.62).
Therefore, in the end, despite the good potential of the selected variable highlighted before,
the mapping of predicted FD or GTD symptomatic vegetation areas over all of the parcels does not
reflect the phytosanitary status observed in the field at all.
For Case 2 (right part of Figure 8), the misclassifications between FD and GTD pixels can concern
a few pixels inside several vegetation areas as it is the case in the example for the Gamay showed here.
However, it can also concern an entire vine vegetation area in other cases (for example, Fer_Servadou
(B) and Duras). In all vineyards, most of the mixed pixels are classified as symptomatic GTD pixels
(see Appendixes A.1 and A.2).
4. Discussion
4.1. Case 1: Discrimination of FD or GTD Symptomatic Vines from Asymptomatic Vines
Using one variable (vegetation index or biophysical parameter) provides promising results to
discriminate symptomatic vine vegetation (FD or GTD) from asymptomatic vine vegetation for red
cultivars. Nevertheless, at the berry color scale, considered as the most operational one, the sensitivity
(0.75) and specificity values (0.80) obtained with the best variable (BP Car) implies that 25% of the
symptomatic pixels are classified as asymptomatic pixels, and 20% of the asymptomatic pixels are
classified as symptomatic pixels. It is definitely not satisfying. The sensitivity value could partly
be explained by the use of symptomatic areas with an infection level of 75–100% in which few
asymptomatic pixels could exist. The specificity value could be explained partly by the presence of
mixed pixels, misclassified in the edges of the rows of vines.
The variables that are most often selected at the vineyard, cultivar and berry color scales
are the vegetation indices RGI/GRVI and CI/NDRE, and the biophysical parameter Car. These
variables are related to the anthocyanins [
25
], chlorophylls [
43
,
53
] and carotenoids [
38
] leaf content,
respectively. This is in agreement with literature as Flavescence dorée and Grapevine Trunk diseases
cause a modification in the photosynthesis process, and therefore in the leaf pigment content. Variation
in anthocyanin content, the main factor explaining discoloration of leaves in red cultivars, was verified
for leaves affected by Flavescence dorée [41].
At the vineyard scale, the vegetation indices RGI and GRVI were also found with the 2015 dataset
acquired over the Gaillac AOC area that focused on symptomatic FD areas vs. asymptomatic areas
discrimination only [
35
]. These VI are based on the green and red spectral bands (RGI, GRVI). These
Remote Sens. 2019,11, 23 19 of 26
results are quite coherent with previous studies as the GRVI was also found as the best index to
discriminate FD symptomatic leaves from healthy leaves for red cultivars at the leaf scale [36].
Concerning the biophysical parameter, the Car, linked to the leaves’ carotenoid content, shows the
best performances. With the 2015 dataset, we found the Ant as the best discriminating parameter [
35
].
We assume this result could be linked to a difference of the reddish coloration intensity observed
in the field, higher in 2015 than in 2016 (see Figure 10). Complementary work is needed to go
further and confirm this assumption. According to the Car values calculated with the Overland
software, symptomatic pixels show higher values than asymptomatic ones for all red cultivars studied.
Few studies [
50
,
54
] have reported that senescent leaves (with FD leaves’ comparable discolorations)
present a lower carotenoid content than green healthy leaves; however, to our knowledge, no study
bringing information about real carotenoid content of FD leaves has been carried out so far. Such
studies would be the only way to validate the consistency of our results.
Figure 10.
Different levels of leaf coloration intensity affected by the Flavescence dorée grapevine
disease for the same cultivar in 2015 and 2016. The images were taken on a vineyard located in
the Gaillac AOC ("Appellation d’Origine Contrôlée" in French or "controlled designation of origin"
in English).
4.2. Case 2: Discrimination of FD from GTD Vines
Discriminating FD from GTD symptomatic vine vegetation using one variable appears to be
more challenging given the discrimination performances observed. It could be interesting only at the
vineyard or cultivar scales where best selected vegetation indices are the RERI and REGI, both using
Red-Edge and Red or Green spectral bands. Concerning the biophysical parameters, AUC values are
either not acceptable (below 0.7) or still low for the best classifiers.
Vegetation indices and biophysical parameters related to the leaf pigment content are not efficient
in discriminating FD and GTD. Both diseases lead to similar pigment variations. The non-specificity of
multispectral variables has also been found by [
31
] in the case of ESCA (grapevine disease) detection
using the NDVI vegetation index. In addition, as we have mentioned, FD symptomatic vines show a
slight discoloration of the leaves in 2016. FD symptomatic vines with a strong discoloration (like those
observed in 2015) are easier to differentiate from GTD symptomatic vines. Further studies could focus
on the areas with stronger discoloration to aim at specific discrimination between FD and GTD.
Whatever the discrimination case, when the selected variables are applied to the whole vineyard,
pixels are misclassified. It implies further work to provide an operational service. The limits identified
are mainly related to the presence of (i) mixed pixels, (ii) symptomatic areas with a low level of
Remote Sens. 2019,11, 23 20 of 26
infection, and (iii) other abnormal factors of leaf discoloration (e.g., apoplexy). These limits have
already been found in the studies on the detection of grapevine leafroll disease [
30
] or ESCA [
31
],
and in the study we carried out in 2015 on Flavescence dorée [35].
5. Conclusions and Perspectives
Flavescence dorée is a grapevine disease with important economic consequences for winegrowers.
The most effective means of control is the uprooting of the vine presenting the symptoms of the disease.
However, the detection of these strains in the vineyard is time-consuming. Use of remote sensing
images acquired from UAVs appears as a fast and accurate tool for detecting the symptomatic vine
foliage. One of the first studies carried out on a dataset acquired in 2015 over four selected vineyards
has highlighted the potentiality of UAV multispectral images to discriminate FD symptomatic
areas from asymptomatic areas [
35
]. This first study also showed the necessity to discriminate
Flavescence dorée from other vineyard diseases presenting similar changes in leaf coloration when
considering red cultivars.
The new study presented in this paper aims at testing the potential of 20 variables (5 spectral
bands, 15 vegetation indices and 4 biophysical parameters) computed from UAV multispectral imagery
to remotely discriminate (1) symptomatic from asymptomatic vines (Case 1) and (2) FD from GTD
symptomatic vine (Case 2). Receiver operator characteristic (ROC) analysis was used to determine
the capacity of each variable to discriminate FD from AS pixels using both univariate classification
approaches. Our proposed method, tested over seven red cultivar vineyards, seems promising to
detect diseases leading to leaf discolorations such as FD and GTD. However, the specific detection
of FD still appears to be limited. A visual analysis of the disease mapped at whole vineyard scale
highlights problems of misclassifications of true symptomatic FD or GTD and asymptomatic pixels,
already observed in the 2015 study. Most of those misclassifications could be related to the presence of
mixed soil/vine vegetation or shadow/vine vegetation pixels. Thus, from an operational perspective,
future work should focus on improving vine vegetation masking. It could be done by using the surface
elevation extracted from UAV images to separate rows from the row spacing as suggested by [
64
].
Another alternative would be the use of image processing algorithm based on dynamic segmentation,
Hough Space Clustering and Total Least Squares techniques proposed by [
65
]. Another option for
improving the results could be working with a better spatial resolution (flying at lower height).
However, the economic cost should be considered for the detection of Flavescence dorée to become
operational. Another work perspective is definitely to analyze in more detail spectral signatures
of vineyard diseases leading to FD similar discolorations that will complete the results obtained by
Guttler and al. [
36
] and Al-saddik and al. [
66
]. This could lead to the development of a specific spectral
index and to suggesting specifications in terms of the number and width of bands to adapt existing
sensors, or build future sensors for enhanced FD detection. This index should be able to take into
account mixed infections and different levels in leaves’ coloration intensity.
Author Contributions:
J.A., S.D., A.J. and H.C. are principal authors of this manuscript. J.A., S.D. and G.D.
conceived and designed the experiments. J.A., S.D., A.J. and H.C. performed the experiments and wrote the
manuscript. J.A., M.G., S.D., J.A. and H.C. analyzed the data and the results. H.P. provided expert knowledge
about image processing and biophysical parameters. J.R. provided expert knowledge about data collection and
vineyard diseases. All authors participated in the discussions, provided comments and suggestions during the
writing of the paper.
Funding:
This research was funded by the regional OENOMIP project which is co-funded by the European Union
and by the PURPAN Engineer school (INP Toulouse, France).
Acknowledgments:
This study was performed at the PURPAN Engineer school (INP Toulouse, France) in close
collaboration with the TerraNIS company (Toulouse, French). It was partly funded within the regional OENOMIP
project which is co-funded by the European Union. We thank the DELAIR company, the Maison des Vins de
Gaillac, the Chamber of Agriculture of Tarn (82), and the Institut Coopératif du Vin group for their help and
support for data collection.
Conflicts of Interest: The authors declare no conflicts of interest.
Remote Sens. 2019,11, 23 21 of 26
Appendix A
Appendix A.1. Map Disease Level of Vineyard Analysis
DS3
DS4 DS1
DS3
DS1
DS2
DS3
DS2
DS3
DS4
DS3
DS2
DS3
DS2
DS4
DS4
DS3
DS2
DS3
DS4 DS1
DS3
DS1
DS2
DS3
DS2
DS3
DS4
DS3
DS2
DS3
DS2
DS4
DS4
DS3
DS2
DS3
DS3
DS4
DS2
DS4
DS3
DS4
DS3
DS1
DS2
DS2
DS1
DS1
DS1
DS1
DS2
DS2
DS2
DS1
DS3 DS3
DS3
DS3
DS4
DS3
DS4
DS2
DS4
DS3
DS1
DS1
DS2
DS1
DS1
DS2
DS1
DS2
DS2
DS1
DS2
DS2
DS3
DS2
DS1
DS4
DS1
DS4
DS4
DS3
DS2
DS2
DS3
DS4
DS2
DS3
DS2
DS1
DS4
DS1
DS4
DS4
DS3
DS2
DS2
DS3
DS4
DS4
DS2
DS3
DS3
DS4
DS3
DS4
DS2
DS3
DS3
DS4
DS3
DS3 DS3
DS4
DS4
DS4
DS2
DS3
DS4
DS3
DS3 DS3
DS4
DS4
DS4
DS2
DS3
DS4
DS3
FD or GTD pixels
AS pixels
FD vines
GTD vines
AS vines
OF vines
FD pixels
GTD pixels
FD vines
GTD vines
AS vines
OF vines
Case 1 : (FD or GTD) and Asymptomatic (AS) vines Case 2 : FD and GTD symptomatic vines
Fer_Servadou (A)Fer_Servadou (B)Gamay (A)Gamay (B)Duras
Car
OT = 5.65
RERI
OT = 0.54
RGI
OT = 0.81
Ant
OT = 2.91
CI
OT = 1.13
ARI
OT = 7.22
RGI
OT = 0.83
REGI
OT = 0.44
Car
OT = 5.58
REGI
OT = 0.48
Level of analysis : Vineyard
Figure A1.
Map disease using the best multispectral variable (Spectral band, vegetation index
or biophysical parameter) calibrated by vineyard to discriminate symptomatic vine vegetation
(Flavescence dorée and Grapevine Trunk Disease) from asymptomatic vine vegetation (AS) (case 1) and
Flavescence dorée vine vegetation (FD) from Grapevine Trunk Disease vine vegetation (GTD) (case 2).
Remote Sens. 2019,11, 23 22 of 26
Appendix A.2. Map Disease Level of Cultivar Analysis
DS3
DS4 DS1
DS3
DS1
DS2
DS3
DS2
DS3
DS4
DS3
DS2
DS3
DS2
DS4
DS4
DS3
DS2 DS2
DS3
DS4 DS1
DS3
DS1
DS2
DS3
DS2
DS3
DS4
DS3
DS2
DS3
DS2
DS4
DS4
DS3
DS3
DS3
DS4 DS2
DS4
DS4
DS3
DS1
DS2
DS2
DS1
DS2
DS1
DS2
DS1
DS2
DS1
DS3 DS3 DS3
DS3
DS4
DS2
DS4
DS4
DS3
DS1
DS1
DS2
DS2
DS1
DS1
DS2
DS2
DS1
DS2
DS4
DS2
DS3
DS2
DS1
DS1
DS4
DS4
DS3
DS2
DS3
DS3
DS4
DS2
DS3
DS2
DS1
DS1
DS4
DS4
DS3
DS2
DS3
DS3
DS2
DS4
DS3
DS2
DS2
DS4
DS4
DS2
DS2
DS4
DS3
DS2
DS2
DS4
DS4
DS2
Car
OT = 5.7
FD or GTD pixels
AS pixels
FD vines
GTD vines
AS vines
RERI
OT = 0.53
FD pixels
GTD pixels
OF vines
FD vines
GTD vines
AS vines
OF vines
FD vines
GTD vines
AS vines
OF vines
FD vines
GTD vines
AS vines
OF vines
Car
OT = 5.64
FD or GTD pixels
AS pixels
REGI
OT = 0.44
FD pixels
GTD pixels
Fer_Servadou (A)Fer_Servadou (B)Gamay (A)Gamay (B)
Case 1 : (FD or GTD) and Asymptomatic (AS) Case 2 : FD and GTD symptomatic vines
Level of analysis : Cultivar
Figure A2.
Map disease using the best multispectral variable (Spectral band, vegetation index or
biophysical parameter) calibrated by cultivar to discriminate symptomatic vine vegetation (Flavescence
dorée and Grapevine Trunk Disease) from asymptomatic vine vegetation (AS) (case 1) and Flavescence
dorée symptomatic vine vegetation (FD) from Grapevine Trunk Disease vine vegetation (GTD) (case2).
Remote Sens. 2019,11, 23 23 of 26
Appendix A.3. Map Disease Level of Berry Color Analysis
DS3
DS4 DS1
DS3
DS1
DS2
DS3
DS2
DS3
DS4
DS3
DS2
DS3
DS2
DS4
DS4
DS3
DS2
DS3
DS3
DS3
DS4
DS2
DS4
DS3
DS4
DS3
DS1
DS2
DS2
DS1 DS1
DS2
DS2
DS1
DS2
DS1
DS4
DS3
DS2
DS3
DS2
DS1
DS1
DS4
DS4
DS3
DS2
DS3
DS3
DS2
DS4
DS2
DS3
DS3
DS4
DS3
DS3 DS3
DS4
DS4
DS4
DS2
DS3
DS4
DS3
DS4
DS3
DS4
DS4
DS4
DS3
DS1
DS3
DS4
DS4
DS1
DS4
DS2
DS1
DS2
DS4
DS4
DS4
DS4
DS1
DS2
DS2
DS3
DS2
DS4
DS3
DS1
DS3
FD or GTD pixels
Asymptomatic pixels
FD vines
GTD vines
AS vines
OF vines
Berry color :
Red
Car
OT = 5.62
Level of analysis : Berry color
Fer_Servadou (A)
Fer_Servadou (B)
Gamay (A)Gamay (B)
DurasMourvèdre
Grenache
Case 1 : (FD or GTD) and asymtomatic vines
Figure A3.
Map disease using the best multispectral variable (Spectral band, vegetation index or
biophysical parameter) calibrated by color to discriminate symptomatic vine vegetation (Flavescence
dorée and Grapevine Trunk Disease) from asymptomatic vine vegetation (AS) (case 1).
Remote Sens. 2019,11, 23 24 of 26
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