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# Estimation of sugar beet resistance to Cercospora Leaf Spot disease using UAV multispectral imagery

S Jay, A Comar, R Benicio, N Henry, Marie Weiss, F Baret

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## To cite this version:

S Jay, A Comar, R Benicio, N Henry, Marie Weiss, et al.. Estimation of sugar beet resistance to Cercospora Leaf Spot disease using UAV multispectral imagery. 7th International Workshop on Image Analysis Methods in the Plant Sciences (IAMPS), Jul 2019, Lyon, France. hal-03140103

HAL Id: hal-03140103

https://hal.inrae.fr/hal-03140103

Submitted on 12 Feb 2021

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Distributed under a Creative Commons Attribution 4.0 International License
# Estimation of sugar beet resistance to Cercospora Leaf Spot disease using UAV multispectral imagery

S. Jay*, A. Comar**, R. Benicio**,
N. Henry***, M. Weiss*, F. Baret*

* CAPTE, UMR EMMAH, INRA, Avignon

** HIPHEN SAS, Avignon

*** Florimond Desprez, Capelle-en-Pévèle

July 4, 2019
Context

▶ Plant diseases, a major issue affecting crop yield,

▶ Several challenges related to plant diseases, including:

▶ (Early) detection of plant disease (precision agriculture),

▶ Assessment of cultivar resistance to disease (cultivar selection or phenotyping).

▶ The Cercospora Leaf Spot (CLS) disease, a major threat for sugar beet plants.

▶ Selection of CLS-resistant cultivars is one of the solutions (with crop rotation and use of fungicides).

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Context

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► The reference method to assess CLS resistance:

► Visual assessment of disease severity by experts at several dates: score ranging from 1 (healthy plants) to 9 (plants destroyed),

► Integration of the disease evolution curve (ADPC = Area under Disease Progression Curve, Shaner & Finney, 1977)

► About the ADPC method:

► (Generally) Accurate,

► Subjective (e.g., inter-expert variability), time-consuming,

► Application for phenotyping is difficult due to the high number of microplots.

► Need for an automatic method to study CLS development.

<figure><img src="image_9.png" /></figure>

<footnote>* G. Shaner, R. Finney (1977). The Effect of Nitrogen Fertilization on the Expression of Slow-Mildewing Resistance in Knox Wheat. *Phytopathology*, 1051-1056.</footnote>
Context

► UAV (Unmanned Aerial Vehicle) multispectral remote sensing, a promising approach for assessing sugar beet resistance to CLS,

► Provides accurate estimates of canopy structure and biochemistry (Jay et al., 2018), e.g.,

► Green Fraction (GF),

► Green Area Index (GAI),

► Canopy Chlorophyll Content (CCC).

► Efficient (in terms of microplots / unit time).

<figure><img src="image_10.png" /></figure>

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hi-phen
INTEGRATED PLANT PHENOTYPING SYSTEMS

<figure><img src="image_12.png" /></figure>

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<figure><img src="image_14.png" /></figure>

<footnote>* S. Jay, F. Baret, D. Dutartre, G. Malatesta, S. Héno, A. Comar, M. Weiss, F. Maupas (2018). Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. *Remote Sensing of Environment*, in press.</footnote>
Context

► Objectives:

► Relating the <u>dynamics of CLS severity</u> as evaluated
from field measurements, with the <u>dynamics of</u>
<u>remote-sensing variables</u>,

► Estimating <u>instantaneous disease scores</u> and <u>sugar beet resistance</u> based on time series of remote-sensing variables.

► Hypothesis:

► The observed decrease in GF/GAI is primarily due to CLS, not to natural senescence (the latter would occur later if no CLS).

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??

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??

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UAV
# Data

<table><thead><tr><th></th><th></th><th>2016</th><th>2017</th><th>2018</th></tr></thead><tbody><tr><td>Number of microplots</td><td></td><td>80</td><td>1374</td><td>1522</td></tr><tr><td rowspan="2">Number of dates</td><td>Field</td><td></td><td></td><td></td></tr><tr><td>UAV</td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_19.png" /></figure>

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hi-phen
INTEGRATED PLANT PHENOTYPING SYSTEMS

<figure><img src="image_23.png" /></figure>

AV

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* Acquisitions in Casteljaloux (47),

* Plants inoculated, not treated (except in 2016)

* Disease scores evaluated by experts (4-6 times),

* UAV images

* 20 m < Flight altitude < 50 m

* AIRPHEN multispectral camera,

* Six bands: 450, 530, 570, 675, 730, 850 nm,

* Spatial resolution ≈ cm.

Estimation of sugar beet resistance
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# Methodology: dynamics of remote-sensing variables

* Two remote-sensing variables estimated for each UAV multispectral image of microplot

* Mean NDVI over all pixels: $NDVI = \frac{R_{850}-R_{675}}{R_{850}+R_{675}}$ (Rouse et al., 1973)

* GF (Green Fraction) estimated by thresholding the VARI (Gitelson et al., 2002) image, as described in Jay et al., (2018)

* For both NDVI and GF variables, three dynamics are compared:

* Dynamics #1: Raw values,

* Dynamics #2: Values normalized by the maximum value,

* Dynamics #3: Values normalized by the maximum value and set to 1 before this maximum.

* The six dynamics were resampled to the dates of visual scoring.

<footnote>* J.W. Rouse *et al.* Monitoring vegetation systems in the great plains with ERTS. Third Earth Resour. Technol. Satell. Symp., 1:309:317, 1973.</footnote>

<footnote>* A.A. Gitelson *et al.* Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ, 80: 76-87, 2002.</footnote>

<footnote>* S. Jay, F. Baret, D. Dutartre, G. Malatesta, S. Héno, A. Comar, M. Weiss, F. Maupas (2018). Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. *Remote Sensing of Environment*, in press.</footnote>
<figure><img src="image_26.png" /></figure>

# Methodology: estimation of instantaneous disease scores and cultivar resistance

* Gaussian Process Regression used to relate instantaneous disease scores and NDVI- and GF-based variables.

* Estimated instantaneous disease scores integrated to estimate ADPC, as for visual scores.

* Estimation performance evaluated using 3-fold cross-validation, e.g., calibration with 2016/2017 and test with 2018, etc (randomly selecting the same number of samples for both years used for calibration x 10)

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# Results: comparing the dynamics

2016
VISUAL SCORES

2016
NDVI

2016
GF

<figure><img src="image_28.png" /></figure>

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2017
VISUAL SCORES

2017
NDVI

2017
GF

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2018
VISUAL SCORES

2018
NDVI

2018
GF

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Data

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GAI = 2,95

GAI = 2,56

GAI = 2,04

GAI = 1,29

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# Results: remotely-sensed GF vs visual scores (#1: raw)

2016
(80 µ-plots)

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2017
(1374 µ-plots)

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2018
(1522 µ-plots)
<figure><img src="image_41.png" /></figure>

Results: remotely-sensed GF
vs visual scores (#2: normalized)

2016
(80 µ-plots)

<figure><img src="image_42.png" /></figure>

2017
(1374 µ-plots)

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2018
(1522 µ-plots)
# Results: remotely-sensed GF vs visual scores (#3: normalized + 1)

2016
(80 µ-plots)

<figure><img src="image_44.png" /></figure>

2017
(1374 µ-plots)

<figure><img src="image_45.png" /></figure>

2018
(1522 µ-plots)
# Results: estimating instantaneous disease scores

<table><thead><tr><th>Remote-sensing variable</th><th>Dynamics type</th><th>2016</th><th>2017</th><th>2018</th><th>All</th></tr></thead><tbody><tr><td>NDVI</td><td>#1: Raw</td><td>36</td><td>48</td><td>33</td><td>39</td></tr><tr><td>GF</td><td>#1: Raw</td><td>35</td><td>50</td><td>20</td><td>35</td></tr></tbody></table>

RMSEP (%)

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RMSEP = 1.2 (26 %), r² = 0.74

Visual score

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RMSEP = 1.2 (28 %), r² = 0.59

Visual score

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RMSEP = 0.7 (14 %), r² = 0.92

Visual score
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# Results: estimating sugar beet resistance (field ADPC)

<figure><img src="image_50.png" /><figcaption>## 2016
ADPC</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>## 2018
VISUAL SCORES</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>## 2017
ADPC</figcaption></figure>

<caption>## 2018
ADPC</caption>
# Results: estimating sugar beet resistance (field ADPC)

RMSEP (%)

<table><thead><tr><th>Remote-sensing variable</th><th>Dynamics type</th><th>2016</th><th>2017</th><th>2018</th><th>All</th></tr></thead><tbody><tr><td>NDVI</td><td>#1: Raw</td><td>24</td><td>37</td><td>11</td><td>21</td></tr><tr><td>GF</td><td>#1: Raw</td><td>24</td><td>39</td><td>10</td><td>24</td></tr></tbody></table>
# Results: estimating sugar beet resistance (field ADPC)

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Guidelines for future
experiments

► Poor estimation of low scores with UAV multispectral imagery,

► Unlike visual scoring, UAV estimation of ADPC requires sufficiently late measurements with scores of 8-9 for all microplots.

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Results: estimating sugar beet
resistance (field ADPC)

<figure><img src="image_55.png" /></figure>
Some conclusions

► Possible to estimate instantaneous disease scores from UAV multispectral imagery,

► High scores > low scores

► Estimated scores used to estimate cultivar resistance to CLS,

► Importance of a proper time coverage for UAV measurements,

► Problem is *not about finding the inflection point*, but about *finding the dates of scores = 8-9*

► Limitation for precision agriculture:

► Poorly sensitive to low scores (< 5) => needs spatial resolution !

► How to distinguish between CLS, powdery mildew, natural senescence...?
Some perspectives

► Comparing UAV and UGV; does spatial resolution help ? (paper to come).

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Some perspectives

► Submillimeter-scale UGV RGB images allow the extraction of additional useful features, e.g.,

► Spot density,

► Spot size.

► Combining both features with GF leads to more accurate estimation of instantaneous scores (especially scores < 5) and ADPC.

<figure><img src="image_60.png" /></figure>

UAV: RMSEP = 575

UGV: RMSEP = 300

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-48 %

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UAV: RMSEP = 442

UGV: RMSEP = 360

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-19 % 22
Thanks for your attention !!