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Virus diseases are of high concern in the cultivation of seed potatoes. Once found in the field, virus diseased plants lead to declassification or even rejection of the seed lots resulting in a financial loss. Farmers put in a lot of effort to detect diseased plants and remove virus-diseased plants from the field. Nevertheless, dependent on the cultivar, virus diseased plants can be missed during visual observations in particular in an early stage of cultivation. Therefore, there is a need for fast and objective disease detection. Early detection of diseased plants with modern vision techniques can significantly reduce costs. Laboratory experiments in previous years showed that hyperspectral imaging clearly could distinguish healthy from virus infected potato plants. This paper reports on our first real field experiment. A new imaging setup was designed, consisting of a hyperspectral line-scan camera. Hyperspectral images were taken in the field with a line interval of 5mm. A fully convolutional neural network was adapted for hyperspectral images and trained on two experimental rows in the field. The trained network was validated on two other rows, with different potato cultivars. For three of the four row/date combinations the precision and recall compared to conventional disease assessment exceeded 0.78 and 0.88 respectively. This proves the suitability of this method for real world disease detection.
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ORIGINAL RESEARCH
published: 01 March 2019
doi: 10.3389/fpls.2019.00209
Edited by:
Sebastien Christian Carpentier,
Bioversity International, Belgium
Reviewed by:
Hans-Peter Mock,
Leibniz-Institut für Pflanzengenetik
und Kulturpflanzenforschung (IPK),
Germany
Daniel Leitner,
University of Vienna, Austria
*Correspondence:
Gerrit Polder
gerrit.polder@wur.nl
Specialty section:
This article was submitted to
Plant Breeding,
a section of the journal
Frontiers in Plant Science
Received: 31 October 2018
Accepted: 07 February 2019
Published: 01 March 2019
Citation:
Polder G, Blok PM,
de Villiers HAC, van der Wolf JM and
Kamp J (2019) Potato Virus Y
Detection in Seed Potatoes Using
Deep Learning on Hyperspectral
Images. Front. Plant Sci. 10:209.
doi: 10.3389/fpls.2019.00209
Potato Virus Y Detection in Seed
Potatoes Using Deep Learning on
Hyperspectral Images
Gerrit Polder1*, Pieter M. Blok1, Hendrik A. C. de Villiers1, Jan M. van der Wolf2and
Jan Kamp3
1Agro Food Robotics, Wageningen University & Research, Wageningen, Netherlands, 2Biointeractions & Plant Health,
Wageningen University & Research, Wageningen, Netherlands, 3Field Crops, Wageningen University & Research, Lelystad,
Netherlands
Virus diseases are of high concern in the cultivation of seed potatoes. Once found in
the field, virus diseased plants lead to declassification or even rejection of the seed lots
resulting in a financial loss. Farmers put in a lot of effort to detect diseased plants and
remove virus-diseased plants from the field. Nevertheless, dependent on the cultivar,
virus diseased plants can be missed during visual observations in particular in an early
stage of cultivation. Therefore, there is a need for fast and objective disease detection.
Early detection of diseased plants with modern vision techniques can significantly
reduce costs. Laboratory experiments in previous years showed that hyperspectral
imaging clearly could distinguish healthy from virus infected potato plants. This paper
reports on our first real field experiment. A new imaging setup was designed, consisting
of a hyperspectral line-scan camera. Hyperspectral images were taken in the field with a
line interval of 5 mm. A fully convolutional neural network was adapted for hyperspectral
images and trained on two experimental rows in the field. The trained network was
validated on two other rows, with different potato cultivars. For three of the four row/date
combinations the precision and recall compared to conventional disease assessment
exceeded 0.78 and 0.88, respectively. This proves the suitability of this method for real
world disease detection.
Keywords: crop resistance, phenotyping, hyperspectral imaging, classification, convolutional neural network,
Solanum tuberosum
Abbreviations: CNN, convolutional neural network; CPU, central processing unit; FCN, fully convolutional network;
FN, false negatives; FP, false positives; GGA, message containing time, position, and fix related data; GNSS, global
navigation satellite system; GPS, global positioning system; GPU, graphical processing unit; NAK, Nederlandse Algemene
Keuringsdienst (Dutch General Inspection Service); NMEA, The National Marine Electronics Association, standard for
GNSS position information; NPK, nitrogen, phosphorus and potassium; PCA, principal component analysis; PVC, polyvinyl
chloride; PVY, potato virus Y; RAL, color standard; RAM, random access memory; RD-New, Local coordinate reference
systems of the Netherlands; ReLU, rectified linear unit; RGB, red, green, blue; RGB-D, red, green, blue and depth; RTK, real-
time kinematic; SVM, support vector machine; TBV, tulip breaking virus; TP, true positives; UAV, unmanned aerial vehicle;
VRS, virtual reference station; WGS-84 - World Geodetic System, latest revision, 1984.
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INTRODUCTION
In temperate regions, the main diseases of the seed potato crop
are caused by viruses and bacterial infections (Dickeya and
Pectobacterium). In the Netherlands, the world’s major supplier
of certified seed potatoes, these two diseases are responsible for
an average 14.5% declassification of seed lots (over the period
2009–2016) and an average 2.3% rejection (source: Dutch General
Inspection Service NAK). This results in a total value decrease of
almost 20 million euros per year for all Dutch producers.
A potato crop can be challenged by various viral pathogens
resulting in a broad spectrum of different symptoms. PVY (genus
Potyvirus, family Potyviridae), is one of the most prevalent and
important viruses in potatoes globally (Valkonen, 2007) and is
in the top–ten of most damaging plant viruses (Scholthof et al.,
2011). Different strains of PVY have been identified that vary in
symptom expression, including mosaic leaf discolorations caused
by PVYO, stipple streak caused by PVYc, necrotic leaf spots
caused by PVYNand PVYNTN and necrotic spots on tubers
caused by PVYNTN (Verma et al., 2016a,b). In the Netherlands,
PVYO, and the recombinant strains PVYNTN and PVYNWi
prevail (Verbeek et al., 2009).
There is a lack of efficient resistance in cultivated varieties,
but symptom expression is variety dependent. Cultivars such
as Russet Norkotah and Shepody rarely show symptoms and if
so, only very mild symptoms. Nevertheless, infections of these
cultivars with PVY often result in a decrease in marketable
yield (Hane and Hamm, 1999). The symptomless infected
plants can also be a reservoir for transmission by aphids
(Draper et al., 2002).
Management of PVY is predominantly based on the use of
certified, pathogen free seed, the exclusion of virus infections
by roguing of symptomatic plants that can serve as inoculum
source, and an early harvest, before winged aphids occur that
spread the virus (Woodford, 1992;Robert et al., 2000). In
addition, sanitizing tools, planters and cultivators, weed control,
in particular of solanaceous species, removal of volunteer potato
plants and the use of mineral oils to reduce spread of aphids,
are used in management practices. Insecticides have a low effect
on the transmission of the virus, as the aphids often transmit
the PVY before they are killed (Shanks and Chapman, 1965;
Gibson et al., 1982;Boiteau et al., 1985;Lowery and Boiteau, 1988;
Boiteau and Singh, 1999;Boquel et al., 2013, 2014).
In order to prevent declassification, farmers put in a lot
of effort to detect diseased plants and remove them before
inspection by the Dutch General Inspection Service (NAK).
The average input of manpower is estimated to be 6,2 h/ha
(Kwin, 2015). Manual selection by visual observations, is labor
intensive and cumbersome, in particular late in the growing
season when the crop is fully developed. The cost related to
plant selection by farmers is about 8 Million euros per year
(40.000 ha, 6,2 h/ha, av. labor cost: €32,50/hr). In addition,
the availability of skilled selection workers is getting more and
more a problem. Specialized farmers that are unable to do the
selection work themselves have increasing problems hiring extra
selection capacity. Furthermore, human inspection is prone to
FNs in which sick/diseased potato plants can be missed. This is
especially the case when inspecting potato varieties with mild
disease symptoms.
Visual crop inspections are done one to three times annually
by staff of national inspection agencies. The reliability of
their visual observations compared with a laboratory assay
(PCR/ELISA) was found to be high (93%) for symptoms caused
by viral diseases (K. Boons, NAK, unpublished results).
Precision agriculture together with computer vision
technologies can be an alternative for human inspection
(Bechar and Vigneault, 2016). High tech vision solutions can
mitigate the concerns from the high labor cost and increasing
potato devaluation costs. If an autonomous machine can replace
a human inspector and meanwhile improve the selection quality,
this might provide a new business model that is based on these
high-tech solutions.
Hyperspectral sensors and imaging techniques have shown a
high potential for providing new insights into plant–pathogen
interactions and the detection of plant diseases (Mahlein et al.,
2018). In hyperspectral imaging, every single pixel consists of
an array of values, corresponding to the reflectance, emission
or transmission at a certain wavelength (Behmann et al.,
2015;van der Heijden and Polder, 2015). Currently, a wide
variety of hyperspectral sensors are entering the market. From
traditionally pushbroom line scan sensors (Polder et al., 2003)
up to miniaturized handheld ‘snap shot’ cameras (Behmann
et al., 2018). From a technical perspective, hyperspectral imaging
has a lot of advantages compared to other visual rating and
detection methods. Hyperspectral sensors are able to measure
pathogen-induced changes in plant physiology non-invasively
and objectively (Thomas et al., 2018).
Several studies showed that hyperspectral imaging is an
especially valuable tool for disease detection in a range of
different crops on different scales from the tissue to the canopy
level (Sankaran et al., 2010;Mahlein et al., 2012;Wahabzada
et al., 2015;Thomas et al., 2018). Atherton and Watson (2015);
Atherton et al. (2017) used hyperspectral remote sensing for
detection of early blight (Alternaria solani) in potato plants
prior to visual disease symptoms. In this case only spectral
information was used as the authors didn’t use an imaging
sensor. For late blight (Phytophthora infestans) detection Ray
et al. (2011) also used a point spectrum approach without using
spatial information. Hu et al. (2016) used hyperspectral imaging
to detect late blight disease on potato leaves successfully, with a
discrimination of 95% between healthy and diseased leaves.
Although virus diseases have a different mechanism by which
they change the plant physiology, virus symptoms can also be
measured using optical techniques. The TBV was successfully
detected in tulip plants using spatial and spectral information
(Polder et al., 2010). Spectral signatures of potato plants infected
with PVY, acquired with an hand-held device, were classified
using a SVM with an accuracy of 89.8% between infected and
non-infected plants (Griffel et al., 2018).
For hyperspectral imaging, the entire system pipeline,
consisting of the type of sensor, the mobile platform carrying
the vision system, and the decision-making process by
data analysis has to be tailored to the specific problem
(Kuska and Mahlein, 2018).
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From the data analysis perspective, the use of multi-scale
datasets of hyperspectral images for plant disease detection or the
scale transfer of prediction models is a very challenging, emerging
topic (Arens et al., 2016;Roscher et al., 2016;Thomas et al., 2018).
Spectral vegetation indices have been shown to be useful for an
indirect detection of plant diseases at a canopy level. Mahlein
et al. (2013) developed specific spectral disease indices for the
detection of diseases in sugar beet plants. Advanced machine
learning algorithms were used in several studies. Rumpf et al.
(2010) developed a SVM classifier for detection of Cercospora leaf
spot, leaf rust and powdery mildew on sugar beet leaves.
Deep Convolutional Neural Networks have proven to be a
powerful tool for disease detection based on RGB color images
(Sladojevic et al., 2016;Fuentes et al., 2017;Pound et al.,
2017). With adaptations this technique can also be successfully
applied to hyperspectral images (Garcia-Garcia et al., 2017).
Chen et al. (2014) proposed a hybrid framework of PCA, deep
learning architecture, and logistic regression for classification of
hyperspectral remote sensing data.
Of particular interest to the present article is the fully
convolutional neural network (FCN) architecture first proposed
by Long et al. (2015). This class of neural network provides
an elegant means of performing semantic segmentation of
images. The core principle underlying this class of neural
network is the replacement of the final fully connected
classification stage of standard CNNs with a pixel-level
segmentation stage which uses convolution and upsampling
techniques to replicate the same computation across an entire
image in one forward pass. As discussed in Garcia-Garcia
et al. (2017), subsequent studies have further elaborated
on the method.
A review of hyperspectral image analysis techniques for the
detection and classification of the early onset of plant disease
and stress is given by Lowe et al. (2017). A central focus of that
review is the utilization of hyperspectral imaging in order to
find additional information about plant health, and the ability to
predict onset of disease.
This paper focuses on the detection of PVY infected potato
plants using hyperspectral imaging and deep learning. A novel
FCN is used to detect plant diseases based on hyperspectral
image data.
MATERIALS AND METHODS
Experimental Field
The experimental field was located in the central polder area of
the Netherlands. This farm land was reclaimed from the sea in
the 1940’s and is a high-quality clay soil. The field was part of
an experimental field of the Dutch General Inspection Service
(NAK) near Emmeloord (Netherlands).
Figure 1 shows an image of the field layout. Tubers were
planted on the 11th of May with an intra-row distance of 33 cm.
Table 1 shows information on the varieties and infections used in
the different rows. Rows 1–3 contained plants that were infected
with bacterial diseases, where rows 4–7 contains plants from 4
different varieties infected with PVY. The PVY infected potato
FIGURE 1 | RGB image of the experimental field, acquired with UAV on June
19, 2017. The length of the rows is 110 m for rows 1–3 and 66 m for rows
4–7. The location is near Tollebeek in the Netherlands (lower left).
TABLE 1 | Description of the test field.
Row Cultivar Row length [m] Number of tubers Infection
1 Kondor 110 333 Erwinia
2 Kondor 110 333 Erwinia
3 Kondor 110 333 Erwinia
4 Rosa Gold 66 200 PVY
5 Lady Claire 66 200 PVY
6 Vermont 66 200 PVY
7 PCR/11 66 200 PVY
batches were collected by the NAK and selected for a fairly level
of infection.
Cultivation practices were applied according to practice. The
fertilization basis consisted of the normal amounts of NPK, with
an additional dosage of Manganese Nitrate (total: 0,2 kg Mn)
and Magnesium Nitrate (total: 1.1 kg Mg).Crop protection to late
blight was limited to 13 applications.
Weather conditions during the growing season were quite
different. After a cold spell in March (before planting), the month
of April was quite normal (cool, limited amount of rain) followed
by a long warm and dry period in May and June. By the end of
June, the weather changed and the month of July was wet and
started cool.
During the course of the experiment, all plants in the field were
visually monitored several times by an experienced inspector of
the NAK.
As can be seen from Table 1, rows 1, 2, and 3 had
mainly bacterial infections, but some natural occasional Y virus
infections occurred. These plants as well as the healthy plants in
the first 3 rows were used for training the CNN. Plants showing
symptoms caused by bacterial pathogens were excluded from
the analysis.
Rows 4–7 contained the virus infected plants. Unfortunately,
plants in row 5 (Lady Claire) appeared to be 100% symptomatic
and plants in row 4 (Rosa Gold) showed more than 95%
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symptomatic plants. Furthermore, Potato Virus X (PVX)
symptoms on row 4 disturbed the manual scores of the crop
experts. The appearance of PVX was confirmed by a laboratory
assay (ELISA). Therefore row 4 and 5 were excluded from the
hyperspectral analysis.
Measurements were done at a weekly interval during the
growing season, starting 6 weeks after planting when plants
just started to cover each other. Due to the late start of the
measurements the symptomatic plants stabilized at 13 for row 6
and 8 for row 7 (Tables 3,4).
Hyperspectral Image Acquisition
Image acquisition was done in a larger scope where several
sensor techniques were explored for disease detection in the
field. An imaging box was designed for measuring the potato
plants. The box consists of two equally sized compartments
(150 cm ×75 cm ×150 cm). The first compartment was
equipped with an RGB-Depth camera while the other was
equipped with a Specim FX10 hyperspectral line scan camera
(wavelength range 400–1000 nm). For this paper we only
focussed on the hyperspectral data. An embedded PC (Nexcom
NISE3500) was installed to acquire the hyperspectral images.
Ambient light was blocked by use of light curtains placed around
the measurement box (Figure 2). The measurement box was
placed 3.1 m in front of a tractor that drove at a constant speed of
300 m per hour (0.08 m/s) during the measurements. Figure 3
shows the system operating in the test field. The FX10 is a
pushbroom hyperspectral line scan sensor. The frame rate was
60 f/s, resulting in an interval of 5 mm in the driving direction of
the tractor. The full sensor resolution of the FX10 is 1024 pixels
in the spatial by 224 bands spectral. In order to improve light
sensitivity and speed, the images were binned by a factor 2 in the
spatial direction and a factor 4 in the spectral direction, resulting
in line images of 512 pixels ×56 pixels (Figure 4). As the bands at
FIGURE 2 | Drawing of the measurement box, consisting of two
compartments, the first one for the hyperspectral camera and the second one
for an RGB-D imaging system. Ambient light is blocked by two rows of rubber
flaps. The box can be mounted on the front of a tractor.
FIGURE 3 | Picture of the system while doing field measurements.
FIGURE 4 | One single hyperspectral line image, horizontally showing the
spatial information of one line, vertically showing the spectral reflection
between 400 and 1000 nm.
the start and the end of the spectrum were noisy, only the central
35 bands were kept for further processing.
Plants were illuminated by 13 Tungsten Halogen lamps
(Osram Decostar 51 PRO, 14 Watt, 10, Dichroic) placed in a
row. This way the plants were evenly illuminated. White and
black references were taken at the start of each measurement
cycle. The white reference object was a gray (RAL 7005) PVC
plate. The black reference was taken with the camera shutter
closed. Reflection images were calculated by:
R=IB
WB
where Iis the raw hyperspectral Image, Bis the black reference
and Wis the White reference.
The total length of the crop rows were 110 m and 66 m for
rows 1–3 and 4–7, respectively, with an interval of 5 mm, a total
number of 22,000 and 13,200 line images per row were acquired.
Geo Referencing
The health status of each potato plant was determined by a crop
expert who visually inspected the plants in the experimental field.
Plants that showed Y-virus disease symptoms were geometrically
stored with a RTK GNSS rover (Hiper Pro, Topcon, Tokyo,
Japan). A VRS signal (06-GPS, Sliedrecht, Netherlands) was
used to guarantee a 0.02 m accuracy on the position estimate.
The crop expert obtained the position of a diseased plant by
placing the rover at the center point of the plant. From the
center point, we constructed a geometric plant polygon using
the intra- (0.33 m) and inter-row (0.75 m) distance of the
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potato crop. The constructed plant polygons were stored for
offline processing.
On each measurement day, we obtained the real-world
position of the hyperspectral line images using the RTK-GNSS
receiver of the tractor (Viper 4, Raven Europe, Middenmeer,
Netherlands). The GGA message of the GNSS receiver (NMEA-
0183), containing the WGS-84 coordinates and the GNSS
precision status, were passed to the embedded PC at a frequency
of 10 Hz and stored for offline processing.
We processed the data in such a way that only the
hyperspectral line images were assessed where a RTK-fix signal
was guaranteed. In this way the exact position of all hyperspectral
images could be determined with a precision of 0.02 m. First,
the obtained GNSS coordinates were translated to 3.1 m in
front of the tractor to correspond to the real-world position of
the hyperspectral images. Furthermore, the WGS-84 coordinates
of the images were projected to the planar coordinate system
of the Netherlands (RD-new). These planar coordinates were
rotated and scaled such that the crop rows were parallel in
the X-direction. For each hyperspectral line, we determined the
health status by checking the intersection between the line and
the geometric plant polygon. Lines that overlapped more than
one polygon were left out of consideration. Figure 5 shows a
plot of the GNSS positions of the RTK rover, labeled as viral or
bacterial infection and the GNSS position of the tractor driving
back and forth over the rows.
Deep Learning
The network used was a fully convolutional neural network
(FCN), but had a non-standard decoder (final) portion. Usually
with FCNs the output is a two-dimensional segmentation. Here,
we outputted a one-dimensional segmentation (it was also a
lower resolution 1D strip).
Because the training data is imbalanced (a lot more healthy
cases than diseased cases), the data was resampled in order
to emphasize diseased examples. As deep learning needs huge
numbers of training data, the available data was enriched by
data augmentation techniques, such as random mirroring and
rotation, as well as randomized changes in image brightness.
The neural network architecture employed was an adaptation
of the FCN family of approaches. FCNs are particularly well
suited to performing semantic segmentation of two-dimensional
images, where one wants to assign a class (such as “human”
or “dog”) to each pixel of the image. FCNs employ only a
FIGURE 5 | The GNSS positions of the RTK rover, labeled as viral or bacterial infection and the GNSS position of the tractor driving back and forth over the rows.
The units on the axes are scaled to half the inter row distance and uniform in both directions.
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combination of convolutions, pooling, unpooling and per-pixel
operations (such as ReLU non-linear activation functions). This
allows FCNs to produce a segmentation for the entire image
in one forward pass, replicating the same computation for each
region of the image.
Instead of a two-dimensional segmentation, we employed
a similar approach to predict a one-dimensional stream of
labels, with the goal of assigning a label to each hyperspectral
line image.
Each of the input images is constructed from 500 consecutive
line scan images, representing a subset of a row captured by the
tractor. From each line scan image of 512 pixels, 250 pixels are
retained. During testing, these are the central 250 pixels, but the
interval is randomly chosen during training as a form of data
enrichment. The process of generating a training or test image
is illustrated in Figure 6.Figure 6E illustrates the annotation
of the input images based on the health status of each potato
plant as determined by the crop expert and stored with the
(RTK-GNSS) rover. Ground truth is indicated in the top band,
with green and red labels indicating healthy or diseased plants,
respectively. Black labels indicate regions excluded from testing
due to labeling uncertainty. Input images have a resolution of
500 ×250. The number of feature maps at the input equals the
number of hyperspectral bands kept (35).
The network architecture is described in Table 2 as a
series of 17 stages, each of which contains a convolutional
layer followed by a number of optional layers. These optional
layers include max pooling, a per-element non-linearity (either
Leaky ReLU activation functions with negative gradient of 0.01,
or sigmoidal), batch normalization and dropout (Figure 7).
A dropout probability of 0.5 was used. A SpatialDropout layer
(Tompson et al., 2015) was used toward the start of the
convolutional portion of the network (within stage 1). Standard
dropout layers are employed in the fully connected portion of the
network (stages 14 through 16).
Stages 1 through 7 are typical of two-dimensional FCNs. Here
we perform a series of convolutions which act as successive
feature extraction stages. Interspersed max pooling stages lower
the feature map resolution, allowing the convolutions to extract
features based on larger regions of image context. The feature
map spatial dimensionality reduces in a symmetrical fashion,
leading to more enriched and discriminating features.
Stages 8 through 13 are similar to earlier stages, except here
the architecture departs from two-dimensional FCNs in that we
now restrict pooling to the dimension parallel with individual
line images. There were two sections (stages 8–10 and 11–13)
containing 3 convolutions each, with both sections ending with
an 1 ×4 max pooling. This had the effect of asymmetrically
shrinking the feature map resolution from 62 ×31 to 62 ×7, and
then finally 62 ×1. We refer to these two stages as “combiners,
as they were meant to combine evidence across a hyperspectral
line (and its close neighbors) until this spatial dimension has
collapsed to a length of 1. Additionally, the two sections shared
the same parameters (as indicated in Table 2), which was
motivated by both these sections performing the operation of
pooling evidence across local regions into larger regions.
Stages 14 through 17 performed the final per-label decision
through a series of convolutions with 1 ×1 kernels. Due to the
kernel dimensions, these were effectively fully connected stages
performed in parallel, and independently, on each output point.
The final layer has a sigmoidal non-linearity, as the output should
be a probability of disease.
FIGURE 6 | Illustration of the procedure for generating a training image. The figure shows the 722 nm wavelength band. The original image is 500 ×512 (A). This
image is then rotated by a random angle uniformly distributed between –10to 10(B). Subsequently a randomly positioned rectangle with dimensions 500 ×250
is cropped from the rotated image (C) to produce the training image (D). The procedure for test images is the same, except there is no rotation step and the
cropping rectangle is always vertically centered. Ground truth labeling is done on line bases (green – healthy, red – diseased, black – unknown) (E).
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TABLE 2 | Neural Network Architecture consisting of 17 stages, each of which contains a convolutional layer followed by a number of optional layers including max
pooling.
Stage number Kernel size Stride Maxpool Non-linearity Batch
normalization
Dropout
(p= 0.5)
Resulting
feature maps
Resulting
resolution
Input 35 500 ×250
17×7 2 ×2 Leaky ReLU Yes Spatial 70 250 ×125
23×3 1 ×1 Leaky ReLU Yes No 70 250 ×125
33×3 1 ×1 Leaky ReLU Yes No 70 250 ×125
43×3 1 ×1 2 ×2 Leaky ReLU Yes No 120 125 ×62
53×3 1 ×1 Leaky ReLU Yes No 120 125 ×62
63×3 1 ×1 Leaky ReLU Yes No 120 125 ×62
73×3 1 ×1 2 ×2 Leaky ReLU Yes No 150 62 ×31
83×3 1 ×1 Leaky ReLU Yes No 150 62 ×31
93×3 1 ×1 Leaky ReLU Yes No 150 62 ×31
10 3×3 1 ×1 1 ×4 Leaky ReLU Yes No 150 62 ×7
11 3×3 1 ×1 Leaky ReLU Yes No 150 62 ×7
12 3×3 1 ×1 Leaky ReLU Yes No 150 62 ×7
13 3×3 1 ×1 1 ×4 Leaky ReLU Yes No 150 62 ×1
14 1×1 1 ×1 Leaky ReLU No Yes 100 62 ×1
15 1×1 1 ×1 Leaky ReLU No Yes 50 62 ×1
16 1×1 1 ×1 Leaky ReLU No Yes 20 62 ×1
17 1×1 1 ×1 Sigmoid No No 1 62 ×1
SpatialDropout was employed in stage 1.
TABLE 3 | Confusion matrices of row 6 (A,B) and 7 (C,D), measured on
2017/06/27 (A,C) and 2017/07/03 (B,D).
Predicted PVY Predicted healthy
A
Known PVY 12 (92.3%) 1 (7.7%)
Known healthy 11 (6.4%) 160 (93.6%)
Recall: 0.92, Precision: 0.52.
B
Known PVY 12 (92.3%) 1 (7.7%)
Known healthy 18 (10.5%) 153 (89.5%)
Recall: 0.92, Precision: 0.4.
C
Known PVY 7 (87.5%) 1 (12.5%)
Known healthy 6 (3.4%) 173 (96.6%)
Recall: 0.88, Precision: 0.54.
D
Known PVY 6 (75%) 2 (25%)
Known healthy 20 (11.1%) 159 (88.9%)
Recall: 0.75, Precision: 0.23.
The output layer produced 62 labels, while there were
500 pixels along the corresponding axis in the input image.
Typically, a two-dimensional FCN would have included a series
of trainable convolutions and unpooling stages to produce a
segmentation at the original input resolution. However, in our
application, we were concerned with labeling entire plants as
healthy or diseased. Since we expect each plant to occupy multiple
sequential labels even at the lower resolution of the output, we
forgo a trainable unpooling network in favor of a simple nearest
neighbor upscaling to 500 pixels to match the input image.
TABLE 4 | Confusion matrices of row 6 (A,B) and 7 (C,D), measured on
2017/06/27 (A,C) and 2017/07/03 (B,D), after correction for neighboring plants.
Predicted PVY Predicted healthy
A
Known PVY 12 (92.3%) 1 (7.7%)
Known healthy 1 (0.6%) 170 (99.4%)
Recall: 0.92, Precision: 0.92.
B
Known PVY 12 (92.3%) 1 (7.7%)
Known healthy 3 (1.8%) 168 (98.2%)
Recall: 0.92, Precision: 0.8.
C
Known PVY 7 (87.5%) 1 (12.5%)
Known healthy 2 (1.1%) 177 (98.9%)
Recall: 0.88, Precision: 0.78.
D
Known PVY 6 (75%) 2 (25%)
Known healthy 14 (7.8%) 165 (92.2%)
Recall: 0.75, Precision: 0.3.
Ground truth labeling made use of GNSS coordinates of
diseased plants (as determined by crop experts). Training and
testing the neural network require labeling of each hyperspectral
line image (either as diseased, healthy or excluded). To
convert the GNSS coordinates to ground truth labeling, the
following procedure was employed. Initially, all line images
were labeled as healthy. Then, for each diseased plant, the
line image with GNSS tag closest to the diseased plant was
located, which acted as a center point for subsequent labeling.
The 150 line images before and after this center line were
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marked as excluded (if not already marked as diseased). Then,
if the infection was specifically of a viral nature, the 50
line images before and after the center point were marked
as diseased.
Because there was non-uniformity in the dimensions
and growth patterns of each individual plant, this labeling
procedure ensured that we labeled only regions where
there was a high degree of certainty regarding the health of
associated plants.
Figure 6 shows an example of such a labeling where a single
plant with a viral infection is located toward the left-hand side
of the image. The core diseased labels and excluded regions can
be seen surrounding this plant, with regions marked as healthy
extending beyond the excluded region.
During training and testing, predictions were produced for all
hyperspectral line images in the input regardless of their labeling.
However, only lines that had ground truth labeling as either
healthy or diseased contributed to error measures used during
FIGURE 7 | Neural Network Architecture consisting of 17 stages, each of which contains a convolutional layer followed by a number of optional layers including max
pooling.
FIGURE 8 | Known PVY infection and CNN predicted infection for row 6 on 2017/06/27 and 2017/07/03.
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training and evaluation of the system, whereas excluded lines did
not contribute.
The model was trained on rows 2 and 3, with row 1 used
as a validation set during the training process. After training
was completed, the resulting neural network was independently
tested on rows 6 and 7.
The model was trained and tested on a system containing an
Intel Xeon E5-1650 CPU with a 3.5 GHz clock speed and 16 Gb of
RAM. The system also housed an Nvidia GTX 1080 Ti GPU with
11 Gb of video memory. All data preprocessing, Deep Learning
training and validation was done using the PyTorch deep learning
framework (Paszke et al., 2017). At test time, a forward pass
through the neural network took a total of 8.4 ms, of which 4.0
and 1.1 ms were the times taken to move data to and from the
GPU, respectively. This time is negligible compared to the time
taken to acquire the images.
RESULTS
Results showed that although there were not much Y Virus
infected plants in the training set, the network performed well
in predicting the infected plants in row 6 and 7. Figures 8,9
show the results for the different measurement dates for row
6 and 7, respectively. In these figures the x-axis shows the line
number in the hyperspectral image of the total row. The y-axis
shows the output of the network as a probability. An average of
network predictions was calculated over all line images associated
with a particular plant, and a probability threshold of 0.15 on
this average was used to distinguish the diseased plants for
prediction (Figure 10).
In Table 3 the confusion matrices after classification are
given for the different rows and measurement dates. From the
confusion matrices the precision (also called positive predictive
value) and recall (also known as sensitivity) were calculated.
Precision is a measure of the fraction of relevant instances,
in this case the diseased plants among the retrieved instances,
while recall (also known as sensitivity) is the fraction of relevant
instances that have been retrieved over the total amount of
relevant instances (Olson and Delen, 2008). Precision and recall
are then defined as:
Precision =tp
tp +fp
Recall =tp
tp +fn
where tp is the true positive fraction, fp is the false positive
fraction and fn is the false negative fraction. For row 6 the
precision is 0.52 and 0.4 for the first and second measurement
week, respectively, and the recall is 0.92 for both weeks. For row
7 the precision measures are 0.54 and 0.23, respectively, the recall
is 0.88 and 0.75, respectively.
FIGURE 9 | Known PVY infection and CNN predicted infection for row 7 on 2017/06/27 and 2017/07/03.
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FIGURE 10 | A threshold of 0.15 was applied to the average predictions for a given plant in order to translate the probability output of the CNN into a decision.
FIGURE 11 | False positives are closely connected to true positives.
Investigation into the position of the FPs shows that most
of them were connected to TPs (Figure 11). When the FPs
connected to TPs were ignored, the confusion matrices are
much better (Table 4). The recall measures stay the same, as
this correction does not affect the FNs. The precision measures
improve to 0.92 and 0.8 for row 6 and 0.78 and 0.3 for
row 7, respectively.
DISCUSSION
During viral infections plants react with changes in
the respiration, photosynthesis, sugar metabolism and
phytohormone activity. This results in (pathological) changes
that are observed at a microscale in the form of an increased
number of mitochondria, a degeneration of chloroplasts and
a thickening of the cell wall. At a macroscale infected plants
show growth reduction, wilting, necrosis, chlorosis and a strong
increase of the autofluorescence (Hinrichs-Berger et al., 1999;
Kogovsek et al., 2010). Current practice is to manually score
the plants in the field using a predefined protocol (NAK)
in which 40% of the plants are scored. The accuracy of an
experienced judge is 93%. Furthermore, certified personnel need
extensive field experience before they can become familiar with
the symptoms induced by various pathogens in many potato
varieties under differing environmental conditions (Shepard and
Claflin, 1975).
In this research we have proven that disease symptoms can
be detected with machine vision techniques using hyperspectral
cameras with a precision which is almost equal to the
accuracy of an experienced crop expert and the possibility of
scanning the whole field compared to 40% as defined by the
NAK protocol.
For classification an FCN was used. Although these networks
are mainly applied to color RGB images, in this study we
demonstrated that a modified Fully CNN can be employed on
hyperspectral images for the task to detect plant diseases. There
are currently very few complete studies applying deep learning to
hyperspectral data, though this is an active research area. Several
challenges need to be addressed in order to use hyperspectral data
for deep learning, including the size of the data and the noisiness
of specific wavelength bands (Lowe et al., 2017).
Typically, CNNs are either used for classifying entire images
(label-per-image) or providing two-dimensional segmentations
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Polder et al. Hyperspectral Imaging for Virus Detection in Potato
(label-per-pixel). Label-per-image approaches generally require
vast amounts of training data to generalize properly, input
image/label combinations easily lead to overfitting. By contrast,
label-per-pixel approaches tend to need smaller input image
datasets, as each labeled pixel becomes, in effect, a training case.
However, obtaining pixel-level labeling can be difficult, especially
if one is searching for subtle image patterns that a human cannot
discern in input images.
Instead of either of these extremes, we showed that a “weak”
one-dimensional label sequence can be used in combination with
a modified FCN architecture for disease detection. This approach
has the advantage of increasing the effective number of labels
(label-per-line) available in the training set, thus lowering the
risk of overfitting. Simultaneously, this approach substantially
lowers the burden of labeling datasets. Instead of being required
to provide pixel-level annotations, one can use GNSS locations
of diseased individuals to generate ground truth on the line-
level, which is a substantially simpler process. We also showed
that the system is robust with respect to uncertainty in the exact
boundaries between neighboring plants.
Results showed that although the training data is limited, the
prediction of PVY is good. The percentage of detected infected
plants, expressed in the recall values, is slightly lower (75–92%)
than the accuracy of the crop expert (93%). Due to the low
percentage of diseased plants, the precision values are worse,
with a range from 0.23 to 0.54 (Table 3). Note that for the
crop expert the number of FPs as expressed in the precision
measures is unknown. A large amount of the FPs are due to
inaccuracy of the positioning system, either on the tractor, or
on the RTK rover positioning device when doing ground truth
measurements. After correction the precision measures almost
doubled for all row/week combinations.
The experiment was setup for detection of both bacterial
and virus diseases in seed potatoes. This paper focuses on the
detection of PVY virus infected potato plants. Detection of
bacterial diseased plants will be reported separately.
The plants were already large and overlapping when the first
imaging experiments started. It is expected that when plants are
smaller and do not overlap, the accuracy of position of the plant
polygons can be greatly improved, which may result in better
performance.
It is important to note that the independent test set consisted
of plants from other cultivars than those in the training and
validation sets. Generally, one does not expect models to
generalize well to new varieties as different growth patterns and
surface characteristics may disturb regularities observed by the
model specific to the training set cultivar. Despite this added
difficulty, the system obtained accuracies with precision larger
than 0.75 for 3 out of 4 row/week combinations in the test
set. This is a strong indication that the system has found real
underlying regularities due to the disease state. We do note
that the system performance for the last measurement week of
row 7 is relatively poor, indicating the need for a follow-up
study to determine the reason for this discrepancy. A potential
reason might be that uninfected plants of this variety also start
deteriorating due to the heat or plant growth stage.
DATA AVAILABILITY
The datasets generated for this study are available on request to
the corresponding author.
AUTHOR CONTRIBUTIONS
GP, PB, JvdW, and JK: contributions to conception and design of
this study. GP, PB, HdV, JvdW, and JK: participation in drafting
and revising of the manuscript. GP and PB: experiments and data
acquisition. JvdW: pathogen inoculation. PB: geo referencing the
data. GP and HdV: hyperspectral data analysis.
FUNDING
This project was funded by: Dutch Topsector Agri&Food
as subproject under AF-14275, Op naar precisielandbouw
2.0, Branche Organisation Arable Farming (BO-Akkerbouw),
Dutch Farmers Organisation (LTO-Nederland), Kverneland
Mechatronics, Agrico, HZPC, and NAK.
ACKNOWLEDGMENTS
We thank Toon Tielen of Wageningen University & Research
(WUR) for building the field imager, Gert-Jan Swinkels (WUR),
and Angelo Mencarelli (WUR) for writing the acquisition
software and Kees Boons (NAK) for providing the test field
and manual scoring of the plants. Youri Schoutsen (WUR)
assisted the field measurements. The UAV acquired RGB image
(Figure 1) has been made available by Lammert Kooistra from
the Unmanned Aerial Remote Sensing Facility (UARSF-WUR).
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2019 Polder, Blok, de Villiers, van der Wolf and Kamp. This is an
open-access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
Frontiers in Plant Science | www.frontiersin.org 13 March 2019 | Volume 10 | Article 209
... Table 5 shows some relevant studies from recent years. For the detection of potato viruses, Polder et al. [101] designed a fully convolutional neural network that was adapted for hyperspectral images and trained on two experimental rows in the field. The trained network was validated on two other rows, with different potato cultivars. ...
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Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases.
... In potato research, detection of diseases through remote sensing has been advanced for late blight (Gold et al. 2020;Ray et al. 2011;Duarte-Carvajalino et al. 2018) and viruses (Chávez et al. 2009;Chávez et al. 2010;Griffel et al. 2018;Polder et al. 2019). Whereas there are clear challenges to implementing such technologies routinely at a landscape or regional level, obvious direct application could be available through automated screening for resistance in breeding trials and rapid screening for diseases in seed multiplication plots. ...
... In the field of plant disease identification, the hyperspectral imaging technology is usually used for object detection because the difference in reflectance of plant disease features is slight (Yue et al., 2015;Polder et al., 2019;Wang D. et al., 2019). The investigation of Nagasubramanian et al. (2019) demonstrated that soybeans infected the charcoal rot are more sensitive than healthy soybeans in the wavelengths of visible spectra (400-700 nm). ...
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... In potato research, detection of diseases through remote sensing has been advanced for late blight (Gold et al. 2020;Ray et al. 2011;Duarte-Carvajalino et al. 2018) and viruses (Chávez et al. 2009;Chávez et al. 2010;Griffel et al. 2018;Polder et al. 2019). Whereas there are clear challenges to implementing such technologies routinely at a landscape or regional level, obvious direct application could be available through automated screening for resistance in breeding trials and rapid screening for diseases in seed multiplication plots. ...
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... In potato research, detection of diseases through remote sensing has been advanced for late blight (Gold et al. 2020;Ray et al. 2011;Duarte-Carvajalino et al. 2018) and viruses (Chávez et al. 2009;Chávez et al. 2010;Griffel et al. 2018;Polder et al. 2019). Whereas there are clear challenges to implementing such technologies routinely at a landscape or regional level, obvious direct application could be available through automated screening for resistance in breeding trials and rapid screening for diseases in seed multiplication plots. ...
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... In potato research, detection of diseases through remote sensing has been advanced for late blight (Gold et al. 2020;Ray et al. 2011;Duarte-Carvajalino et al. 2018) and viruses (Chávez et al. 2009;Chávez et al. 2010;Griffel et al. 2018;Polder et al. 2019). Whereas there are clear challenges to implementing such technologies routinely at a landscape or regional level, obvious direct application could be available through automated screening for resistance in breeding trials and rapid screening for diseases in seed multiplication plots. ...
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... In potato research, detection of diseases through remote sensing has been advanced for late blight (Gold et al. 2020;Ray et al. 2011;Duarte-Carvajalino et al. 2018) and viruses (Chávez et al. 2009;Chávez et al. 2010;Griffel et al. 2018;Polder et al. 2019). Whereas there are clear challenges to implementing such technologies routinely at a landscape or regional level, obvious direct application could be available through automated screening for resistance in breeding trials and rapid screening for diseases in seed multiplication plots. ...
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The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative and technology-based optical methods for plant disease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-invasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on different scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated. The advantages and disadvantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant–pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantification, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets.