=Paper=
{{Paper
|id=Vol-2696/paper_140
|storemode=property
|title=Overview of LifeCLEF Plant Identification task 2020
|pdfUrl=https://ceur-ws.org/Vol-2696/paper_140.pdf
|volume=Vol-2696
|authors=Hervé Goëau,Pierre Bonnet,Alexis Joly
|dblpUrl=https://dblp.org/rec/conf/clef/GoeauBJ20
}}
==Overview of LifeCLEF Plant Identification task 2020==
https://ceur-ws.org/Vol-2696/paper_140.pdf
Overview of LifeCLEF Plant Identification task
2020
Hervé Goëau1,2 , Pierre Bonnet1,2 , and Alexis Joly3,4
1
CIRAD, UMR AMAP, France, herve.goeau@cirad.fr, pierre.bonnet@cirad.fr
2
AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
3
Inria ZENITH team, France, alexis.joly@inria.fr
4
LIRMM, Montpellier, France
Abstract. Automated identification of plants has improved consider-
ably thanks to the recent progress in deep learning and the availability
of training data with more and more photos in the field. However, this
profusion of data only concerns a few tens of thousands of species, mostly
located in North America and Western Europe, much less in the richest
regions in terms of biodiversity such as tropical countries. On the other
hand, for several centuries, botanists have collected, catalogued and sys-
tematically stored plant specimens in herbaria, particularly in tropical
regions, and the recent efforts by the biodiversity informatics community
made it possible to put millions of digitized sheets online. The LifeCLEF
2020 Plant Identification challenge (or ”PlantCLEF 2020”) was designed
to evaluate to what extent automated identification on the flora of data
deficient regions can be improved by the use of herbarium collections.
It is based on a dataset of about 1,000 species mainly focused on the
South America’s Guiana Shield, an area known to have one of the great-
est diversity of plants in the world. The challenge was evaluated as a
cross-domain classification task where the training set consist of sev-
eral hundred thousand herbarium sheets and few thousand of photos to
enable learning a mapping between the two domains. The test set was
exclusively composed of photos in the field. This paper presents the re-
sources and assessments of the conducted evaluation, summarizes the
approaches and systems employed by the participating research groups,
and provides an analysis of the main outcomes.
Keywords: LifeCLEF, PlantCLEF, plant, domain adaptation, cross-domain
classification, tropical flora, Amazon rainforest, Guiana Shield, species identi-
fication, fine-grained classification, evaluation, benchmark
1 Introduction
Automated identification of plants and animals has improved considerably in
the last few years. In the scope of the LifeCLEF 2017 Plant Identification chal-
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). CLEF 2020, 22-25 Septem-
ber 2020, Thessaloniki, Greece.
lenge [14] in particular, impressive identification performance were measured
thanks to recent deep learning models (e.g. up to 90 % classification accuracy
over 10K species), and it was shown in [13] that automated systems are nowa-
days not so far from the human expertise. However, these conclusions are valid
for species that are mostly living in Europe and North America. Therefore, the
LifeCLEF 2019 Plant identification challenge was focused on tropical countries,
where there are typically much less of collected observations and images and
where the flora is much more difficult to identify for human experts [7].
In the meantime, biodiversity informatics initiatives such as iDigBio5 or e-
ReColNat6 made available online millions of digitized herbarium sheets collected
over several centuries and conserved in many natural history museums over the
world. During centuries, botanists have collected, catalogued and systematically
stored plant specimens in herbaria. These physical specimens are used to study
the variability of species, their phylogenetic relationship, their evolution, or phe-
nological trends. One of the key step in the workflow of botanists and taxonomists
is to find the herbarium sheets that correspond to a new specimen observed in
the field. This task requires a high level of expertise and can be very tedious.
Developing automated tools to facilitate this work is thus of crucial importance.
In the continuity of the PlantCLEF challenges organized in previous years [4–12],
the LifeCLEF 2020 Plant identification challenge presented in this paper was de-
signed to evaluate to what extend automated identification on the flora of data
deficient regions can be improved by the use of natural history collections of
herbarium sheets. Many species in tropical countries are not easily accessible,
resulting in a very limited number of photos collected in the field, while sev-
eral hundred or even several thousand of herbarium sheets have been collected
over the centuries. Herbaria collections represent potentially a large pool of data
to train species prediction models, but they also induces a much more difficult
problem usually referred as a cross domain classification task. Indeed, a plant
photographed in the field may have a very different visual appearance than its
dried version placed on a herbarium sheet (as illustrated in Figure 1).
2 Datasets and task description
2.1 Training set
The conducted study was based on a newly created dataset of 997 species mainly
focused on the Guiana shield and the Northern Amazon rainforest (see Figure
2), an area known to have one of the greatest diversity of plants and animals
in the world. The dataset contains 321,270 herbarium sheets (see Table1 for
detailed information). About 12% were collected in French Guyana and hosted
in the ”Herbier IRD de Guyane” (IRD Herbarium of French Guyana). These
herbarium sheets were digitized in the context of the e-ReColNat6 project. The
5
http://portal.idigbio.org/portal/search
6
https://explore.recolnat.org/search/botanique/type=index
Fig. 1. Photos in the field and herbarium sheets of the same individual plant (Tapirira
guianensis Aubl.). Despite the very different visual appearances between the two types
of images, similar structure and shapes of flowers, fruits and leaves can be observed.
remaining herbarium sheets come from the iDigBio5 portal (the US National
Resource for Advancing Digitization of Biodiversity Collections).
In order to enable learning a mapping between the two domains (i.e. between
the ”source” domain of herbarium sheets and the ”target” domain of field pho-
tos), a relatively smaller set of 6,316 photos in the field was provided additionally
to the large herbarium sheets dataset. About 62 % of them also come from he
iDigBio portal and were acquired by various photographers related to numer-
ous institutes and national museums that share their data in iDigBio. Besides,
two highly trusted experts of the French Guyana flora, Marie-Françoise Prévost
”Fanchon” [3] and Jean-François Molino7 provided the remaining field photos
that were divided between the training set and the test set.
A valuable asset of the training set is that a set of 354 plant observations are
provided with both herbarium sheets and field photos for the same individual
plant. This potentially allows a more precise mapping between the two domains
(see previous Figure 1 as an example).
It should also be noted that about half of the species in the training set (495
to be precise) is only represented by herbarium sheets and therefore it is not
possible to learn to recognize them directly from field photos.
2.2 Test set
The test set was composed of 3,186 photos in the field related to 638 plant
observations (about 5 pictures per plants on average). To avoid bias related to
similar pictures coming from neighboring plants in the same observation site, we
ensured that all observations of a given species by a given collector were either
in the training set or in the test set but never spread over the two sets. For
instance, for the observations of J.F. Molino, the 166 species in the test set are
different from the 125 species in the training set.
Most importantly, plant species in the test set were selected according to
the number of field photos illustrating them in the training set. As it can be
7
https://scholar.google.fr/citations?user=xZXYc4kAAAAJ&hl=fr
Fig. 2. Density grid maps by number of species of geolocated plant specimens in the
PlantCLEF2020 dataset. Many species have also been collected in other regions outside
French Guiana, over a large part of the Americas, but also in Africa for some of them.
Table 1: Details of the PlantCLEF 2020 dataset according to the origin of the
pictures and their domain (herbarium sheets or field photos).
Origin Domain Used as #Pictures #Species
Herbier IRD de Guyane Herbarium sheets Train 38,552 631
iDigBio Herbarium sheets Train 282,718 991
iDigBio Field photos Train 3,935 426
Fanchon Field photos Train 1,130 183
Molino Field photos Train 1,251 125
Fanchon Field photos Test 1,830 271
Molino Field photos Test 1,356 166
Train (all) Herbarium sheets Train 321,270 997
Train (all) Field photos Train 6,316 502
Test (all) Field photos Test 3,186 408
observed in Figure 3 (a), the priority was given to species with few or no field
pictures at all. Such a choice may seem drastic, making the task extremely
difficult, but the underlying idea was to encourage and promote methods that
are as generic as possible, capable of transferring knowledge between the two
domains, even without any examples in the target domain for some classes. The
second motivation of this choice, was to impose a mapping between herbarium
and field photos and avoid that classical methods based on CNNs perform well
because of an abundance of field photos in the training set rather than the use
of herbarium sheets above all.
2.3 External training sets
Participants to the evaluation were allowed to use complementary training data
(e.g. for pre-training purposes) but on the condition that (i) the experiment is
Fig. 3. Species according to the estimated number of images for each domain in the
training set (in blue). Each species is surrounded by an additional orange circle if it
is used in the test set, and a red circle if used in the test subset of difficult species
(with few field photos according to the PlantCLEF 2020 the training set). The bottom
graph revises the positions of the species by including additional training pictures from
external datasets that could be used by the participants. It is estimated that most of
the species related to the difficult test subset have less than 10 field photos.
entirely reproducible, i.e. that the used external resource is clearly referenced
and accessible to any other research group, (ii) the use of external training data
or not is clearly mentioned for each evaluated method, and (iii) the additional
resource does not contain any of the test observations. External training data
was thus allowed but participants had to provide at least one submission that
used only the training data provided this year.
2.4 Task Description
The goal of the task was to identify the correct species of the 638 plant of the
test set. For every plant, the evaluated systems had to return a list of species,
ranked without ex-aequo. Each participating group was allowed to submit up to
10 run files built from different methods or systems (a run file is a formatted
text file containing the species predictions for all test items).
The main evaluation measure for the challenge was the Mean Reciprocal
Rank (MRR), which is defined as the mean of the multiplicative inverse of the
rank of the correct answer:
Q
1 X 1
Q q=1 rankq
where Q is the number of plant observations and rankq is the predicted rank of
the true label for the qth plant observation.
A second evaluation measure was again the MRR but computed on a subset
of observations of difficult species that are rarely photographed in the field. The
species were chosen based on the most comprehensive estimates possible from
different data sources (iDigBio, GBIF, Encyclopedia of Life, Bing and Google
Image search engines, that were actually provided by the organizers or some
participants of previous editions of PlantCLEF and ExpertCLEF challenges). It
is therefore a more challenging metric because it focuses on the species which
impose a mapping between herbarium and field photos. Figure 3 (b) revises
the previous Figure 3 (a) according to the considered external data sources and
shows that many plant observations in the difficult test subset are related to
species estimated to have less than 10 field photos.
Course of the challenge: The training data was publicly shared in early
February 2020 through the AICrowd platform8 . Any research team wishing to
participate in the evaluation could register on the platform and download the
data. The test data was shared in mid-April but without the species labels, which
were kept secret. Each team could then submit up to 10 submissions correspond-
ing to different methods or different settings of the same method. A submission
(also called a run) takes the form of a csv file containing the predictions of the
method being evaluated for all observations in the test set. For each submission,
the calculation of the evaluation metrics is then done automatically and visible
to the participant. Once, the submission phase was closed (mid-June), the par-
ticipants could also see the evaluation metric values of the other participants. As
8
https://www.aicrowd.com/challenges/lifeclef-2020-plant
a last important step, each participant was asked to provide a working note, i.e.
a detailed technical report containing all technical information required to repro-
duce the results of all submissions. All LifeCLEF working notes are reviewed by
at least two members of LifeCLEF organizing committee to ensure a sufficient
level of quality and reproducibility.
3 Participants and methods
68 participants registered for the PlantCLEF challenge 2020 and downloaded the
data set, and 7 research groups succeeded in submitting a total of 49 runs, i.e.
files containing the predictions of the system(s) they ran. Details of the meth-
ods are developed in the individual working notes of most of the participants
(LU [21], ITCR PlantNet [20], Neuon AI [2] and SSN [16]). The other teams did
not provide a detailed description of their systems, but some informal descrip-
tions were sometimes provided in the metadata associated with the submissions
and partially contributed to the comments below.
LU, Lehigh University, USA, 10 runs [21]: this team used a Partial Domain
Adaptation (PDA) approach corresponding to the scenario where target cate-
gories are only a subset of source categories to promote positive transfer. They
first extracted deep features from a pre-trained NASNetLarge model [22] and
find the shared categories between the two domains. They then develop an novel
Adversarial Consistent Learning (ACL) approach through an unified deep ar-
chitecture which combine a source domain classification loss, an adversarial loss
and a feature consistent loss. The adversarial loss helps to learn domain-invariant
features while the feature consistent loss aims to preserve the fine-grained fea-
ture transition between the two domains.
Neuon AI, Malaysia, 7 runs [2]: this team developed a triplet loss net-
work [1, 18] between herbarium sheets and field images, trained to maximize the
embedding distances of different species while minimizing the embedding dis-
tances of same species. First, two InceptionV4 CNNs [19] were fine-tuned, one
exclusively with the herbarium sheets related to the 997 classes, and an other
one with more than 1 million pictures related to 10k classes from the PlantCLEF
2017 dataset [5]. The networks are then fused in a final embedding layer trained
to optimize the distances between two embeddings based on triplet loss, but
only on the subset of 435 classes that contain both herbarium sheets and field
photos in the PlantCLEF 2020 training set. Then, each class is associated to a
single embedding computed as the average of embeddings from random herbar-
ium sheets of the class. For inference, a plant observation is then associated to
a single embedding computed as the average of the embeddings from all field
photos of the observation. The Cosine similarity is used as a distance metric
between the embeddings of all the herbarium classes and the embedding of the
tested field observation. It is then transformed with Inverse Distance Weighting
into probabilities for ranking the classes. The best results was reached with an
ensemble of 3 triplet loss networks, without any frozen layers and many data
augmentations techniques on both sides (training and test pictures).
ITCR - PlantNet, Costa Rica & France, 10 runs, [20]: this team based
most of its runs on a Few Shot Adversarial Domain Adaptation approach [15]
(FSADA) where the purpose is to learn a domain agnostic feature space while
preserving the discriminative ability of the features for performing the species
classification task. First, a ResNet50 is finetuned in the herbarium sheets only.
Then, the encoder part of the ResNet50, without the classifier last layers, is
frozen and used to extract features on herbarium sheets or field photos. Then,
given random pairs of extracted features, a discriminator is trained to distinguish
4 categories: (1) different domains and different classes, (2) different domains and
same class, (3) same domain and different classes, (4) same domain and same
classes. Finally, during a last stage, the encoder, the discriminator and the classi-
fier are trained together. Domain adaptation is achieved once the discriminator
is not able to distinguish samples from categories (1) and (2) and categories
(3) and (4), when the discriminator is not able to tell which was the original
domain. This participant attempted several improvements based on a jigsaw
self-supervision technique or/and the use of the taxonomic information provided
in the metadata (with multi-task classifiers and multiple discriminators). The
best result was obtained with an ensemble of several variations of FSADA mod-
els, while the best single model was using 3 taxonomic levels (species, genus,
family) and external datasets (PlantCLEF 2019 [7] and GBIF [17]).
SSN College of Engineering, India, 2 runs, [16]: this team used a classical
CNN approach based on a ResNet50 which didn’t give very good results given
the limited number of field photos in the training set. It seems they didn’t use
any external data.
The 3 other teams did not provide an extended description of their system.
According to the description provided in the submission system, the UWB team
(3 runs) used a classical CNN approach based on ResNet18 with various combi-
nations of external data [17]. Unfortunately, at the time of writing, there is not
enough information about the submissions from the ”To Be” (10 runs) and the
”Domain” teams (7 runs).
4 Results
We report in Figure 4 and Table 4 the performances achieved by the 49 evalu-
ated runs. Figure 5 reorganizes the results according to the second MRR metric
focusing on the most difficult species.
The main outcomes we can derive from that results are the following ones:
A very difficult task even with the most advanced deep learning tech-
niques. The best MRR value obtained across all evaluated methods was equal to
Table 2: Results of the LifeCLEF 2020 Plant Identification Task
Team run MRR (whole test set) MRR (difficult species)
ITCR PlantNet Run 10 0.180 0.052
ITCR PlantNet Run 9 0.170 0.039
ITCR PlantNet Run 8 0.167 0.060
ITCR PlantNet Run 6 0.161 0.037
ITCR PlantNet Run 4 0.148 0.039
ITCR PlantNet Run 7 0.143 0.036
ITCR PlantNet Run 5 0.134 0.062
Neuon AI Run 7 0.121 0.107
ITCR PlantNet Run 2 0.112 0.013
Neuon AI Run 5 0.111 0.108
Neuon AI Run 3 0.103 0.094
Neuon AI Run 2 0.099 0.076
Neuon AI Run 6 0.093 0.066
Neuon AI Run 4 0.088 0.073
Neuon AI Run 1 0.081 0.061
ITCR PlantNet Run 3 0.054 0.039
UWB Run 2 0.039 0.007
UWB Run 3 0.039 0.007
LU Run 8 0.032 0.016
LU Run 10 0.032 0.016
LU Run 9 0.032 0.016
Domain Run 2 0.031 0.015
Domain Run 6 0.029 0.015
Domain Run 4 0.028 0.015
To Be Run 10 0.028 0.016
To Be Run 9 0.028 0.014
Domain Run 1 0.028 0.007
LU Run 5 0.027 0.008
Domain Run 5 0.026 0.014
LU Run 7 0.025 0.007
LU Run 6 0.025 0.008
Domain Run 3 0.024 0.015
UWB Run 1 0.024 0.011
To Be Run 7 0.019 0.007
Domain Run 7 0.019 0.012
To Be Run 2 0.016 0.007
To Be Run 8 0.015 0.005
To Be Run 6 0.014 0.009
LU Run 2 0.011 0.004
LU Run 3 0.011 0.004
To Be Run 5 0.011 0.009
LU Run 4 0.009 0.007
LU Run 1 0.009 0.006
SSN Run 2 0.008 0.003
SSN Run 1 0.008 0.003
To Be Run 1 0.006 0.005
To Be Run 3 0.006 0.005
To Be Run 4 0.006 0.005
ITCR PlantNet Run 1 0.002 0.002
Fig. 4. PlantCLEF 2020 evaluation results based on the primary evaluation metric,
i.e. the Mean Reciprocal Rank over the entire test set.
Fig. 5. PlantCLEF 2020 evaluation results based on the secondary metric, i.e. the
Mean Reciprocal Rank over the subset of difficult species with few or no field photos
in the training set.
0.18. This is quite low compared to the MRR value measured within more clas-
sical plant identification benchmarks, e.g. MRR=0.92 in LifeCLEF 2017 Plant
Identification challenge [14] and MRR=0.36 LifeCLEF 2019 Plant Identification
challenge focused on tropical flora [7]. As already noticed, tropical flora is in-
herently more difficult than generalist flora (even for experts), which partially
explains the low performance achieved. The asymmetry between training data
based on herbarium sheets and test data based on field photos adds a consider-
able difficulty. It is important to note that the low scores achieved do not mean
that the use of herbarium sheets does not improve the identification. The species
in the test set were actually selected as the ones having very few field pictures in
the training set. The performance that would have been obtained on that species
without using any herbarium sheet would have been considerably weaker.
Traditional CNNs performed poorly. Figure 4 shows a great disparity be-
tween the performances obtained by the different evaluated methods. To explain
that we have first to distinguish between approaches based on classical CNNs
alone (typically pre-trained on ImageNet and fine-tuned with the provided train-
ing data) and approaches that additionally incorporate an explicit and formal
domain adaptation technique between the herbarium and field domains. As ex-
pected regarding the low number of field photos in the training set for numerous
species, directly fine-tuned CNNs with the PlantCLEF 2020 training set ob-
tained the lowest scores (ITCR PlantNet Run 1, SSN Run 1&2, UWB Run 1).
Moreover, the use of external training data with classical CNNs did not
greatly improve performances. It provides some improvements on the main
evaluation metric as demonstrated with the UWB runs 2 & 3 and ITCR PlantNet
Run 2. All these runs extended the training data with the PlantCLEF 2019 train-
ing data [7] and the GBIF training data provided by [17]). ITCR PlantNet Run
2 made a greater improvement on the main MRR metric probably because they
used a two stage training strategy: they first fine-tuned a pre-trained ResNet50
with all the herbarium sheets from PlantCLEF 2020, and then finetuned it again
with all the field photos PlantCLEF 2020 and the external training data (Plant-
CLEF 2019 and GBIF). This two stages strategy can be seen eventually as a first
naive domain adaptation technique because the second stage shifts the learned
features in an initial herbarium feature space to a field feature space. However,
regarding the second MRR metric focusing on the most difficult species with few
field photos in the training set, performances for all the aforementioned runs is
still quite low. This means that the performances of a classical CNN approach,
without a formal domain adaptation technique, is too dependent from the num-
ber of field photos available in the training data, and is not able to efficiently
transfer visual knowledge from the herbarium domain to the field photos domain.
An adversarial domain adaptation technique performed the best. Among
other submissions, two participants stood out from the crowd with two differ-
ent domain adaptation techniques. ITCR PlantNet team based all its remaining
runs on a Few Shot Adversarial Domain Adaptation approach [15] (FSADA),
directly applied in the ITCR PlantNet Run 3. FSADA approach uses a discrim-
inator that helps the initial encoder trained on herbarium sheets to shift the
learned feature representations to a domain agnostic feature space where the
discriminator is no longer able to distinguish if a picture comes from the herbar-
ium or the field domain, while maintaining the discriminative power regarding
the final species classification task. The basic FSADA approach (ITCR Plant-
Net Run 3) clearly outperformed the traditional CNN approach (ITCR PlantNet
Run 1), while both approaches are based on the same initial finetuned ResNet50
model on the herbarium training data. It should be noted that the LU team also
used an adversarial approach but with less success.
A mapping domain adaptation techniques reached an impressive gener-
icity on difficult species. While the adversarial domain adaptation technique
used by the ITCR PlantNet team obtained the best results on the overall MRR
metric, the Neuon AI team obtained the best results on the second MRR metric
focusing on the most difficult species in the test set. Contrary to the approach
used by ITCR which tries to learn a common and agnostic feature space to both
domains, the Neuon team tries on its side to finetune two networks dedicated
each to one of the domain and to optimize a distance that maps the two domains
for the purpose of classifying species. The Neuon AI Run 5, which is an ensemble
of 3 instances of their approach, gave particularly impressive results with fairly
high and, more importantly, equivalent values for both MRR measures. It means
that Neuon AI’s approach is very robust to the lack of training field photos and
able to generalize on rare difficult species in the test set. In other words, their
approach is able to transfer knowledge to rare species which was the underlying
objective of the challenge.
External data improved domain adaptation approaches. ITCR Plant-
Net Run 4 shows a significant impact on the main MRR metric from using
external training data compared to the same adversarial domain adaptation ap-
proach (ITCR PlantNet Run 3), while maintaining the same level of genericity
on rare species with similar MRRs value on the second metric. Unfortunately it
is not possible to measure this impact on the Neuon AI method because they
did not provide a run using only this year’s training data.
Multi-task approaches have a positive impact on performance. Some
teams implemented multi-task approaches, i.e. they added additional tasks than
the main species identification task in the global optimization problem. Such
approaches are known to potentially improve the performance of the main task
by providing additional knowledge to the model and help the extraction of po-
tentially useful common features. The use of upper taxon level information, in
particular, was successful in ITCR PlantNet Run 6 (using a multi-classification
task integrated to the FSADA approach) compared to ITCR PlantNet Run 4
using only the species classification task. Noticeably, yhis is the first time over
all LifeCLEF plant identification challenges that we clearly observe an impor-
tant gain of the use of genus and family information to improve the species
identification. Many species with few training data have apparently been able
to benefit indirectly from a ”sibling” species with many data related to a same
genus or family. The impact is probably enhanced this year because of the lack
of visual data on many species. To a lesser extent, self supervision auxiliary task
such as jigsaw solving prediction task (ITCR PlantNet Run 5) improved a little
the baseline of this team (ITCR PlantNet Run 4), and the best submission over
all this year challenge is an ensemble of all FSADA approaches, combining self
supervision or not, upper taxons or not.
5 Conclusion
This paper presented the overview and the results of the LifeCLEF 2020 plant
identification challenge following the 9 previous editions conducted within CLEF
evaluation forum. This year’s task was particularly challenging, focusing on
species rarely photographed in the field in the northern tropical Amazon. The
results revealed that the last advances in domain adaptation enable the use of
herbarium data to facilitate the identification of rare tropical species for which
no or very few other training photos are available. A mapping domain adap-
tation technique based on a two-streamed Herbarium-Field triplet loss network
reached an impressive genericity by obtaining quite high similar results regard-
less of whether the species have many or very few field photos in the training
set. On the other hand, a Few Shot Adversarial Domain Adaptation technique
outperformed all the other approaches according to the main metric but not
with the same genericity according to the second metric, even if the use of tax-
onomic information can improve the genericity. The results are thus contrasted
and allow us to hope for improvements in the near future on both aspects: raw
performances and genericity. We believe that the proposed task may be in the
future a new baseline dataset in the field of domain adaptation, and motivate
new contributions through a realistic and crucial usage for the plant biology
research community.
Acknowledgements This project has received funding from the French Na-
tional Research Agency under the Investments for the Future Program, referred
as ANR-16-CONV-0004 and from the European Union’s Horizon 2020 research
and innovation program under grant agreement No 863463 (Cos4Cloud project).
This work was supported in part by the Microsoft AI for Earth program.
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