Early detection of Zymoseptoria tritici infection on wheat leaves using hyperspectral imaging data

Abstract

This article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease. Leaves of two durum wheat genotypes were studied under controlled conditions to track the evolution of Lb disease and capture significant spectral and spatial differences until the onset of symptoms. Hyperspectral image acquisitions were purchased with two cameras in visible-near infrared (VNIR) and short-wave infrared (SWIR) spectral ranges on eighteen dates between one day before inoculation and twenty days after inoculation. For each wavelength range studied, a total of 1175 images provided information on 3326 leaves measured throughout the experiment. These data are valuable since they can be used as a basis to monitor disease's development over time, to build leaf classification models according to their infection status per genotype per day, to develop prediction models related to symptoms' appearance, or to test imaging and spectral analysis methods.

Full text

Data in Brief 59 (2025) 1114 04
Contents lists available at ScienceDirect
Data in Brief
journal homepage: www.elsevier.com/locate/dib
Data Article
Early detection of Zymoseptoria tritici infection
on wheat leaves using hyperspectral imaging
data
Lorraine Latchoumane
a , b , ∗
,Martin Ecarnot
a , c
,Ryad Bendoula
b
,
Jean-Michel Roger
b , c
, Silvia Mas-Garcia
b
, Heloïse Villesseche
b
,
Flora Tavernier
a
, Maxime Ryckewaert
d
, Nathalie Gorretta
a
,
Pierre Roumet
a
,Elsa Ballini
e
a
AGAP, INRAE, Univ Montpellier, CIRAD, Institut Agro, Montpellier, France
b
ITAP, INRAE, Institute Agro, University Montpellier, Montpellier, France
c
ChemHouse Research Group, Montpellier, France
d
LIRMM, Inria, Univ. Montpellier, CNRS, Montpellier, France
e
PHIM, Institut Agro, INRAE, CIRAD, Univ Montpellier, IRD, Montpellier, France
a r t i c l e i n f o
Article history:
Received 19 November 2024
Revised 12 February 2025
Accepted 12 February 2025
Available online 18 February 2025
Dataset link: Early detection of septoria
infection on wheat leaves using
hyperspectral imaging data
(Original data)
Keywo rds:
Plant disease
VNIR
SWIR
Hyperspectral images
Multivariate analysis
Disease monitoring
a b s t r a c t
This article presents a hyperspectral imaging (HSI) database
of healthy leaves and leaves infected with Zymoseptoria trit-
ici fungal pathogen responsible for leaf blotch (Lb) disease.
Leaves of two durum wheat genotypes were studied under
controlled conditions to track the evolution of Lb disease and
capture significant spectral and spatial differences until the
onset of symptoms. Hyperspectral image acquisitions were
purchased with two cameras in visible-near infrared (VNIR)
and short-wave infrared (SWIR) spectral ranges on eigh-
teen dates between one day before inoculation and twenty
days after inoculation. For each wavelength range studied,
a total of 1175 images provided information on 3326 leaves
measured throughout the experiment. These data are valu-
able since they can be used as a basis to monitor disease’s
development over time, to build leaf classification models
according to their infection status per genotype per day, to
∗Corresponding author .
E-mail address: lorraine.latchoumane@inrae.fr (L. Latchoumane).
https://doi.org/10.1016/j.dib.2025.111404
2352-3409/© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
( http://creativecommons.org/licenses/by/4.0/ )

2 L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4
develop prediction models related to symptoms’ appearance,
or to test imaging and spectral analysis methods.
©2025 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY license
( http://creativecommons.org/licenses/by/4.0/ )
Specifications Table
Subject Analytical Chemistry: Spectroscopy
Specific subject area Temp oral and spectral kinetic of fungal infection of wheat leaves using
hyperspectral imaging
Type of data Raw data: VNIR and SWIR hyperspectral images (.hyspex) and header files
(.hdr)
Reconstructed images (.jpg)
Position of pixels (.csv)
Description files (.csv)
Data collection The experiment wa s conducted on two genotypes of durum wheat
( Triticum turgidum durum ) cultivated under controlled conditions in a
growth chamber. Hyperspectral images were collected in planta for
twenty-two days to monitor leaf blotch disease caused by the fungal
pathogen Zymoseptoria tritici . Images were acquired on the first ligulated
leaf in
a non-destructive way using two devices: HySpex VNIR-1800 and
HySpex SWIR-384 (Norsk Elektro Optikk, Norway).
Data source location Institution: Institut National de Recherche pour l’Agriculture, l’Alimentation
et l’Environnement (INRAE)
City: Montpellier
Country: France
Data accessibility Repository name: Data INRAE
Data identification number: doi: 10.57745/WVP0FJ
Direct URL to data: https://doi.org/10.57745/WVP0FJ
1. Value of the Data
• This dataset depicts the visual appearance and spectral information related to the onset ki-
netics of Lb disease symptoms on wheat leaves using hyperspectral images acquired post-
inoculation.
• The images captured are valuable to monitor the evolution of the Lb disease on wheat leaves
through the development of predictive models based on infection status.
• These data can be used to train, test, evaluate, and compare multiple classification models
for Lb disease detection at an early stage utilizing the features of hyperspectral images.
• This dataset will improve our insights of the spatial distribution of symptoms associated with
Lb disease, enabling researchers to develop phenotyping tools, to optimized farming systems
and to guide future experiments.
• Early detection of Lb disease, based on an understanding of host-pathogen interactions, can
help reduce losses, prevent the spread of disease and improve overall crop yields.
• The present dataset is of particular interest in various domains such as phenotyping, phy-
topathology, remote sensing, precision agriculture and chemometrics.
2. Background
Wheat ( Triticum turgidum L.) is the second most widely-cultivated cereal in the world with
803 million tonnes (Mt) produced in 2022 [ 1 ]. The European Union is the major producer, with
France and Germany the two leader countries growing 34.6 Mt and 22.6 Mt of wheat, respec-
tively [ 2 , 3 ]. The UN-FAO states that to meet global food demand, agricultural production must

L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4 3
increase by 50% by 2050. This can be achieved by developing improved cultivars and imple-
menting innovative management practices. To meet future demand, wheat production will need
to increase significantly, despite the risks posed by a changing climate to current production
rates [ 4 ].
Leaf blotch (Lb), previously referred to as Septoria tritici blotch (STB), is the most important
and damaging foliar disease on durum wheat ( Triticum turgidum subsp durum ) provoked by the
pathogenic fungus Zymoseptoria tritici (also identified as Mycosphaerella graminicola for sexual
stage) [ 5 ]. The prevalence of fungal diseases affecting wheat has resulted in a wheat fungicide
market in Europe of over 1.3 billion euros, 70% of which is devoted to Lb management [ 6 ]. Early
detection and protection against diseases are crucial for disease control, crop yield improvement,
cost reduction and increased agricultural output. However, disease diagnosis by traditional meth-
ods is frequently subjective, laborious and time-consuming. In order to prevent rapid extension
of diseases, and thus limit economical losses while reducing chemical fungicide supplies, in-
novative techniques are developed [ 7 ]. Among them, hyperspectral imaging (HSI) offers rapid,
cost-efficient and non-destructive analysis capable to detect plant disease at an early state [ 8 , 9 ].
Automatic, more accurate and non-invasive detection will also facilitate and accelerate the large-
scale evaluation of new resistant varieties or alternative products to fungicides [ 10 ]. HSI has al-
ready been applied in laboratory or under in-fied conditions to assess plant spectral and spatial
changes in plant when infected [ 9 ], including wheat. Indeed, several research studies have been
conducted on wheat using HSI for the detection of foliar disease caused by fungal pathogens,
such as powdery mildew [ 11 ], yellow rust [ 12 ] or Fusarium head blight [ 13 ]. Wheat infection
induced by Z. tritici was also studied using HSI [ 14–16 ]. However, these studies focus mainly
on vegetation indices and supervised methods, such as partial least squares discriminant anal-
ysis (PLS-DA), support vector machine (SVM) and random forest (RF), among others. Moreover,
in our study, measurements have been done on 18 different dates, giving a temporal sampling
of the evolution of infected or non-infected leaves using HSI. In the present article, HSI acquisi-
tions were performed using visible-near infrared (VNIR) and short-wave infrared (SWIR) spectral
ranges on both healthy and manually infected wheat leaves. This approach has led to original
spatio-temporal measurements, combining SWIR and VNIR with imaging for early detection of
Z. tritici on wheat leaves. These data thus provide relevant information for the study of spectra
at the pixel level for supervised and unsupervised models associated with Lb disease on durum
wheat leaves.
3. Data Description
The present dataset contains “Data annotation” and “Data” folders. The directory organiza-
tion structure is shown in Fig. 1 . The folder “Data annotation” gathers a description file (.csv)
of the whole dataset and two SWIR and VNIR table files (.csv) providing information about the
samples and the number of leaves collected per images each day. The folder “Data” contains
VNIR and SWIR hyperspectral images in separate “VNIR” and “SWIR” folders. Each of these di-
rectories includes 18 compressed folders (.zip) corresponding to the dates on which the images
were acquired such as ‘YYYYMMDD’. In these folders, multiple sub-folders are gathered and dif-
ferentiated according to the label of the samples ‘NUM_SIDE’, with ‘NUM’ corresponding to the
number of the plant pot (from 01 to 48), and ‘SIDE’ the measured side of the pot (D for the right
side and G for the left side). Images are located in their corresponding sub-folders and stored
in ENVI format. Two file types are distinguished for each labelled images: the raw image data
(.hyspex) and the metadata file (.hdr) which provides details about the acquisition. The pixels
coordinates of each leaf per image are summarized in separated table files (.csv), with the first
column indicating the vertical coordinate of the pixel, while the second indicates the horizontal
coordinate of the pixel. Finally, an image (.jpg) reconstructed from the hyperspectral image is
also contained in related sub-folders.

4 L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4
Fig. 1. Tree structure of dataset.
Fig. 2. Experimental set-up for HSI acquisitions, showing the leaf arrangement per pot (left), the VNIR camera (top right)
and the SWIR camera (bottom right).
4. Experimental Design, Materials and Methods
4.1. Hyperspectral image acquisition
Images were acquired on 18 dates using two hyperspectral cameras in pushbroom mode
( Fig. 2 ) to provide images of two-dimensional spatial variables (pixels) and one-dimensional
spectral variables (wavelengths). VNIR acquisitions were conducted using the HySpex VNIR-1800

L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4 5
Fig. 3. Visualization images from VNIR (A) and SWIR (B) hyperspectral images. A: RGB image was reconstructed using
waveleng ths 446 nm, 535 nm and 627 nm of VNIR image. B: grey level image was reconstructed using wavele ngth 994
nm of SWIR image.
(Norsk Elektro Optikk, Norway), providing 216 wavelength spectra covering a spectral range from
411 nm to 993 nm and a resolution of 1370 ×102 4 pixels, except for the last date which had
a resolution of 2740 ×2048 pixels. SWIR acquisitions were conducted using the HySpex SWIR-
384 (Norsk Elektro Optikk, Norway), providing 256 wavelength spectra covering a spectral range
from 964 nm to 2494 nm and a resolution of 420 ×320 pixels. The camera-to-leaf acquisition
distance was set at 21.5 cm, with a lens focal distance of 30 cm. Pixel dimensions were 93.4 μm
for the x-axis and 87.6 μm for the y-axis for the VNIR camera, while they were 217 μm for the
SWIR camera for both x- and y-axis.
Samples were placed on a black support material next to a certified reflectance standard
reference (SRS99, Spectralon®) used to normalize spectra and avoid non-linearities in all instru-
mentation components. For each image, the intensity I( λ) of the reflected light and the intensity
I0
( λ) of the light reflected by the standard reference were measured. The signal without light
was represented by the dark current Id
( λ). For every sample, a reflectance image R( λ) was sub-
sequently computed. We individually adjusted each pixel’s reflectance per line as following:
R(
λ)
=
I(
λ)
−Id
(
λ)
I0
(
λ)
−Id
(
λ)
Hyperspectral images (.hyspex) directly contain the corrected intensity of the dark current.
To enable visualization of the VNIR hyperspectral image, an RGB image was constructed using
wavelengths 446 nm (B), 535 nm (G) and 627 nm (R). For SWIR, the monochromatic image at
994 nm was used ( Fig. 3 ).
4.2. Biological materials and experimental design
Hyperspectral images were captured on two susceptible durum wheat genotypes originating
from the David et al. [ 17 ]. Evolutionnary Pre-breeding pOpulation (EPO), i.e. EPO_67 and EPO_68.
For each genotype, 192 seeds were selected by weighing to ensure that each population was
homogenous. The seeds were then sown in 10 ×10 ×10 cm pots in Neuhaus S potting soil with
pozzolan (5L/70L) supplemented with Flocoat retardant fertiliser (70L/90g of soil). Six plants
per pot were arranged in groups of three on opposite right and left sides of the pot. A total
of 48 pots were made available: 24 for genotype EPO_067 and 24 for genotype EPO_068. All
plants were cultivated in 2021 under controlled conditions in a growth chamber at 23 °C with a
photoperiod corresponding to 12 hours of light per day [ 18 ].
Wheat leaves infection were performed using the strain P1a of Z. tritici responsible for Lb dis-
ease [ 18 ]. For each genotype, wheat plants of 12 pots, identified as “Infected”, were inoculated

6 L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4
Fig. 4. VNIR hyperspectral images (label 45_G) illustrating Leaf blotch disease symptoms of wheat leaves infected with
Zymoseptoria tritici at 12 dpi (A), 13 dpi (B) and 14 dpi (C). Necrosis and pycnidia (black dots) caused by the disease are
highlighted in the white inset.
Fig. 5. Experimental timetable.
with P1a dissolved in distilled water/Tween 20 mixture at a concentration of 1.10 6 spores/mL,
while wheat plants of the other 12 pots, identified as “Non-infected”, were inoculated with dis-
tilled water/Tween 20 mixture only. The latter represents the non-infected control, compared
to the infected leaves. Inoculation was carried out 14 days after sowing on 8 cm of the adax-
ial side of the first ligulated leaf using a brush. Inoculation area was delimited using marker.
After inoculation, transparent bags were placed on the plants for three days to keep them in
high humidity conditions to promote Lb infection. The disease was monitored for up to 20 days
post-inoculation (dpi) and symptoms of infection were analyzed by visual inspection throughout
the experiment. Symptoms of Lb disease were assessed by the progressive formation of necrosis
starting from 7 dpi and the emergence of pycnidia (asexual fruiting bodies) in the final stages
of infection ( Fig. 4 ). The expertise of a plant pathologist confirmed the presence of Lb disease at
the end of the experiment. In addition, some leaves were sampled on 5 dates (4 dpi, 7 dpi, 10
dpi, 14 dpi and 17 dpi) for biomolecular analyses to ensure that symptoms observed on infected
wheat leaves were indeed caused by the inoculated pathogen strain.
When possible, hyperspectral images were acquired under controlled laboratory conditions
on each ligulated leaf of the three individual plants for both sides of the pot. HSI was purchased
on 18 dates, starting one day before inoculation and ending 20 dpi ( Fig. 5 ). No HSI acquisition
was carried out at 1 dpi, 2 dpi and 3 dpi due to bagging, and at 9 dpi due to technical prob-
lems. As previously mentioned, leaf sampling was conducted through the experiment, lowering
the number of samples acquired per day as the experiment progressed. In addition, the natural
process of senescence, mechanical injuries and/or technical errors contributed to the decrease in
sample numbers since they were removed from the dataset. In total, 1175 images were acquired
in both SWIR and VNIR spectral ranges, proving data for 3326 leaves in each case ( Table 1 ).

L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4 7
Tabl e 1
Number of images and leaves per treatment in SWIR and VNIR datasets.
Spectral range Treatment Number of hyperspectral images Number of wheat leaves
SWIR Infected 588 1652
Non-infected 587 1674
Tota l 117 5 3326
VNIR Infected 588 1652
Non-infected 587 1674
Tota l 117 5 3326
Fig. 6. Average spectra of infected (purple) and non-infected (green) wheat leaves per day using VNIR spectral range.
4.3. Data analysis
HSI processing and multivariate analysis were performed under MATLAB® R2021a (The Math-
works, Natick, MA, USA) software. Images were reframed in order to focus on the inoculated area
only, resulting in images of dimension 1370 ×781 pixels and 420 ×306 pixels for VNIR and SWIR
images respectively. Pixels located on the inoculated leaf area were then extracted. Leaf pixel
coordinates are summarized in (.csv) files and were used for spectral analysis. The average spec-
trum of each leaf was calculated, then the average spectra of infected and non-infected leaves
per date were calculated and illustrated for VNIR ( Fig. 6 ) and SWIR ( Fig. 7 ) spectral ranges.

8 L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4
Fig. 7. Average spectra of infected (purple) and non-infected (green) wheat leaves per day using SWIR spectral range.
Limitations
Plant senescence appeared earlier than expected, coinciding with the onset of symptoms. This
might be explained by light conditions that accelerated the growth of wheat plants, thus reduc-
ing leaf life. It led to difficulties in assessing Lb disease, which can be confused with senescence.
A confidence level of the disease was then set in place.
Ethics Statement
The authors confirm that they have adhered to the ethical requirements for publication in
Data in Brief. The present research study did not involve any human or animal subjects and did
not use data collected from social media platforms.
Data Availability
Early detection of septoria infection on wheat leaves using hyperspectral imaging data
(Original data) (Data INRAE)
CRediT Author Statement
Lorraine Latchoumane: Conceptualization, Formal analysis, Software, Visualization, Writ-
ing – original draft; Martin Ecarnot: Investigation, Methodology, Data curation, Writing –re-

L. Latchoumane, M. Ecarnot and R. Bendoula et al. / Data in Brief 59 (2025) 11140 4 9
view & editing, Supervision; Ryad Bendoula: Investigation, Methodology, Data curation, Writ-
ing –review & editing, Supervision; Jean-Michel Roger: Writing –review & editing, Supervi-
sion; Silvia Mas-Garcia: Writing –review & editing, Supervision; Heloïse Villesseche: Investiga-
tion, Methodology, Data curation; Flora Tavernier: Investigation, Data curation; Maxime Ryck-
ewaert: Writing –review & editing; Nathalie Gorretta: Supervision; Pierre Roumet: Investi-
gation, Methodology, Data curation, Supervision; Elsa Ballini: Investigation, Methodology, Data
curation, Writing –review & editing, Supervision.
Acknowledgement
This work was supported by the Convergences Digital Agriculture Institute (“DigitAg”) under
the reference 16-CON-0 0 04.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal rela-
tionships that could have appeared to influence the work reported in this paper.
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