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Plant identification: Man vs. Machine
LifeCLEF 2014 plant identification challenge
Pierre Bonnet ·Alexis Joly ·Herv´e
Go¨eau ·Julien Champ ·Christel
Vignau ·Jean-Fran¸cois Molino ·Daniel
Barth´el´emy ·Nozha Boujemaa
Received: date / Accepted: date
Abstract This paper reports a large-scale experiment aimed at evaluating
how state-of-art computer vision systems perform in identifying plants com-
pared to human expertise. A subset of the evaluation dataset used within
LifeCLEF 2014 plant identification challenge was therefore shared with vol-
unteers of diverse expertise, ranging from the leading experts of the targeted
flora to inexperienced test subjects. In total, 16 human runs were collected and
evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge.
One of the main outcomes of the experiment is that machines are still far from
outperforming the best expert botanists at the image-based plant identifica-
tion competition. On the other side, the best machine runs are competing with
experienced botanists and clearly outperform beginners and inexperienced test
subjects. This shows that the performances of automated plant identification
P. Bonnet
CIRAD, UMR AMAP, France, E-mail: pierre.bonnet@cirad.fr
A. Joly, J. Champ
LIRMM, France, E-mail: alexis.joly@inria.fr
H. Go¨eau, A. Joly
Inria ZENITH team, France, E-mail: herve.goeau@inria.fr
J. Champ
INRA UMR AGAP, France, E-mail: julien.champ@lirmm.fr
C. Vignau
Tela Botanica, France, E-mail: christel@tela-botanica.org
J.-F. Molino
IRD, France, E-mail: jean-francois.molino@ird.fr
D. Barth´el´emy
CIRAD, BIOS Direction and INRA, UMR AMAP, F-34398, France, E-mail:
daniel.barthelemy@cirad.fr
N. Boujemaa
INRIA, Direction of Saclay Center, France, E-mail: nozha.boujemaa@inria.fr
2 Pierre Bonnet et al.
systems are very promising and may open the door to a new generation of
ecological surveillance systems.
Keywords visual plant identification ·human evaluation ·digital data ·
image analysis
1 Introduction
Building accurate knowledge of the identity, geographic distribution and uses
of plants is essential for a sustainable development of agriculture as well as
for biodiversity conservation. Unfortunately, such basic information is often
only partially available for professional stakeholders, teachers, scientists and
citizens, and often incomplete for ecosystems that possess the highest plant
diversity. Speeding up the collection and integration of raw botanical observa-
tion data is therefore a crucial challenge. One of the major problems towards
this objective, sometimes referred as the taxonomic gap, is that identifying
plant species is usually impossible for the general public, and often a very dif-
ficult task for professionals, such as farmers or foresters. On the other side, the
number of experienced botanists is decreasing significantly so that collecting
massive sets of plant observations with a valid taxonomic name becomes more
and more challenging.
In this context, content-based image retrieval and computer vision approaches
are considered as one of the most promising solutions to help bridging the
gap, as discussed in [1,2,3, 4, 5]. We therefore see an increasing interest in this
transdisciplinary challenge in the multimedia community (e.g. in [6,7, 8,9,10,
11]). Beyond the raw identification performances achievable by state-of-the-
art computer vision algorithms, recent visual search paradigms actually offer
much more efficient and interactive ways of browsing large flora than standard
field guides or online web catalogs [12]. Smartphone applications relying on
such image-based identification services are particularly promising for setting-
up massive ecological monitoring systems, involving thousands of contributors
at a very low cost.
A first step in this way has been achieved by the US consortium behind LeafS-
nap1, an i-phone application allowing the identification of 184 common amer-
ican plant species based on pictures of cut leaves on an uniform background
(see [13] for more details). Then, the French consortium supporting Pl@ntNet
[5] went one step further by building an interactive image-based plant identi-
fication application that is continuously enriched by the members of a social
network specialized in botany. Inspired by the principles of citizen sciences
and participatory sensing, this project quickly met a large public with more
than 200K downloads of the mobile applications [14,15]. A related initiative
is the plant identification evaluation task organized since 2011 in the context
of the international evaluation forum CLEF2; the task is based on the data
1http://leafsnap.com
2http://www.clef-initiative.eu
Plant identification: Man vs. Machine 3
collected within Pl@ntNet. In 2011, 2012 and 2013 respectively 8, 11 and 12
international research groups participated in this large collaborative evalua-
tion by benchmarking their image-based plant identification systems (see [16,
17,18] for more details). The data used during these 3 first years can be ac-
cessed online3. Contrary to previous evaluations reported in the literature, the
key objective was to build a realistic task very close to real-world conditions
(different users, areas, periods of the year, important species number, etc.).
This paper is directly in the continuity of this initiative as it builds on the
analysis of the 2014th edition of the task organized with the newly created
lab of CLEF LifeCLEF4. 74 research groups worldwide registered and 10 of
them crossed the finish line by submitting runs (from 1 to 4 depending on the
teams). Details of the participants and the methods used in the runs are syn-
thesised in the overview working note of the task [19]. The main contribution
of this new paper is to complete the system-oriented evaluation of LifeCLEF
by a user trial aimed at comparing the performances of state-of-the-art auto-
matic recognition systems with the human expertise (see section 3). A random
selection of observations of the test set was realised and submitted to a panel
of volunteers in the aim to allow strict comparison between computer vision
systems and human expertise. Additionally (and complementary), we also ex-
tended the raw official results of the benchmark by a deeper analysis of the
runs with regard to the image quality of the test observations (see section 2).
2 Analysis of LifeCLEF plant identification task results
2.1 Dataset
The data used for the plant task of LifeCLEF (and all previous plant tasks
of ImageCLEF) is collected through TelaBotanica5, the main French-speaking
botany network linking 20K registered members living worldwide in more than
70 countries. About 41% of the members declare being novice in botany, while
30% declare having a good practice and 7% to be experts. 53% of the mem-
bers have a professional activity related to botany. The main activities of the
network are organized around tens of collaborative projects coordinated by ex-
perts and dedicated to distinct botany issues (plant usages, distribution, iden-
tification, specific flora, etc.). One of them was specifically launched in 2010 to
collect training images to be used for automatic identification purposes. The
botanical target of the project has been gradually increased from leaf scans of
French trees at the beginning to the whole French flora and multiple views of
plants nowadays. The expected images are defined by an acquisition protocol
that is disseminated to the members of the project through illustrated book-
lets containing full-text descriptions as well as positive and negative examples
of the different view types (full plant, flower, leaf, fruit, bark, leaf scan).
3http://publish.plantnet-project.org/project/plantclef
4http://www.lifeclef.org
5http://www.tela-botanica.org
4 Pierre Bonnet et al.
Once raw image data of plants have been collected, they are integrated through
a collaborative tagging tool allowing to enter the taxon’s name and view type
of each observation, as well as complementary metadata (area, author, quality
rating, project identifier, etc.). It is possible to upload unidentified images of
plants so that other members of the social network can determine the right
species. Besides, contradictory determinations can be added to the observa-
tions posted and identified by other users. Each image can therefore be as-
sociated to several determinations made by distinct members and the best
consensus is computed by voting. People who made contradictory determina-
tions can discuss through a forum associated to each ambiguous image and
the determinations can be revised anytime. Finally, the quality of each image
with respect to a given protocol can also be entered by several users through
a rating box. For the LifeCLEF evaluation campaign, observations were con-
sidered as valid only if (i) at least two persons annotated the content (ii) there
is no unresolved conflict on the determination. We finally kept only the 500
species with the more observations (the minimum being 5 observations).
The resulting PlantCLEF 2014 dataset is composed of 60,962 pictures be-
longing to 19,504 observations of 500 species of trees, herbs and ferns living in
Western Europe (present in France, and neighboring countries). This data was
collected by 1,608 distinct contributors. Each picture belongs to one and only
one of the 7 types of views reported in the meta-data (entire plant, fruit, leaf,
flower, stem, branch, leaf scan) and is associated with a single plant observa-
tion identifier allowing to link it with the other pictures of the same individual
plant (observed the same day by the same person). It is noticeable that most
image-based identification methods and evaluation data proposed in the past
were so far based on leaf images (e.g. in [13,20,21] or in the more recent meth-
ods evaluated in [17]). Only few of them were focused on images of flower as in
[6] or [33]. Leaves are far from being the only discriminant visual key between
species but, due to their shape and size, they have the advantage to be easily
observed, captured and described. More diverse parts of the plants however
have to be considered for accurate identification. As an example, the 6 species
depicted in Figure 1 share the same French common name of ”laurier” even
though they belong to different taxonomic groups.
The main reason is that these shrubs, often used in hedges, share leaves
with more or less the same-sized elliptic shape. Identifying a laurel can be very
difficult for a novice by just observing leaves, while it is indisputably easier
with flowers. Beyond identification performances, the use of leaves alone has
also some practical and botanical limitations. Leaves are not visible all over
the year for a large fraction of plant species. Deciduous species, distributed
from temperate to tropical regions, can’t be identified by the use of their leaves
over different periods of the year. Leaves can be absent (i.e. leafless species),
too young or too degraded (by pathogen or insect attacks), to be exploited
efficiently. Moreover, leaves of many species are intrinsically not informative
enough or very difficult to capture (needles of pines, thin leaves of grasses,
huge leaves of palms, ...).
Another originality of the PlantCLEF dataset is that its social nature makes
Plant identification: Man vs. Machine 5
Fig. 1 6 plant species sharing the same common name for laurel in French, belonging to
distinct species.
it closer to the conditions of a real-world identification scenario: (i) images
of the same species are coming from distinct plants living in distinct areas
(ii) pictures are taken by different users that might not have used the same
protocol to acquire the images (iii) pictures are taken at different periods in
the year. Each image of the dataset is associated with contextual meta-data
(author, date, locality name, plant id) and social data (user ratings on image
quality, collaboratively validated taxon names, vernacular names) provided
in a structured xml file. The gps geo-localization and the device settings are
available in many images.
Table 2 gives some examples of pictures with decreasing averaged user ratings
for the different types of views. Note that the users of the specialized social
network creating these ratings (Tela Botanica) are explicitly asked to rate the
images according to their plant identification ability and their accordance to
the pre-defined acquisition protocol for each view type. This is not an aesthetic
or general interest judgement as in most social image sharing sites.
2.2 Task Description
The task was evaluated as a plant species retrieval task based on multi-image
plant observations queries. The goal is to retrieve the correct plant species
among the top results of a ranked list of species returned by the evaluated
system. Contrary to previous plant identification benchmarks, queries are not
defined as single images but as plant observations, meaning a set of one to sev-
eral images depicting the same individual plant, observed by the same person,
the same day. As illustrated in Figure 3, each image is associated with one
view (entire plant, branch, leaf, fruit, flower, stem or leaf scan) and with con-
textual meta-data (data, location, author). Semi-supervised and interactive
approaches were allowed but as a variant of the task and therefore evaluated
independently from the fully automatic methods. None of the participants,
however, used such approaches in the 2014 campaign.
6 Pierre Bonnet et al.
Fig. 2 Examples of PlantCLEF pictures with decreasing averaged users ratings for the
different types of views
In practice, the whole PlantCLEF dataset was split in two parts, one for
training (and/or indexing) and one for testing. The training set was delivered
to the participants in January 2014 and the test set two months later so that
participants had some time to become familiar with the data and train their
systems. After the delivery of the test set, participants had two additional
months to run their system on the undetermined plant observations and finally
send their results files. Participants were allowed to submit up to 4 distinct
runs. More concretely, the test set was built by randomly choosing 1/3 of the
observations of each species whereas the remaining observations were kept in
the reference training set.
This resulted in a dataset presented in Table 2.2. The xml files containing
the meta-data of the query images were purged so as to erase the taxon name
(the ground truth) and the image quality ratings (that would not be available
at query stage in a real-world mobile application). Meta-data of the observa-
tions in the training set were kept unaltered.
In practice, each candidate system is then evaluated through the submis-
sion of a run, i.e. a file containing a set of ranked lists of species (each list
corresponding to one query observation and being sorted according to the
Plant identification: Man vs. Machine 7
Fig. 3 Query examples – some of them are composed of a single picture whereas some
others contain several pictures of one or several types of view
Dataset #images Branch Entire Flower Fruit Leaf Stem Scan
& scan-like
Train 47815 1987 6356 13164 3753 7754 3466 11335
Test 13146 731 2983 4559 1184 2058 935 696
Table 1 Statistics on the number of images by datasets, and by image types
confidence score of the system in the suggested species). The metric used to
evaluate the submitted runs is an extension of the mean reciprocal rank [34]
classically used in information retrieval. The difference is that it is based on a
two-stage averaging rather than a flat averaging such as:
S=1
U
U
X
u=1
1
Pu
Pu
X
p=1
1
ru,p
(1)
where Uis the number of users (within the test set), Puthe number of
individual plants observed by the u-th user (within the test set), ru,p is the
rank of the correct species within the ranked list of species returned by the
evaluated system (for the p-th observation of the u-th user). Note that if the
correct species does not appear in the returned list, its rank ru,p is considered
as infinite. Overall, the proposed metric allows compensating the long-tail dis-
tribution effects of our social data. As any social network, few people actually
produce huge quantities of data whereas a vast majority of users (the long
tail) produce much less data. If, for instance, only one person did collect an
important percentage of the images, the classical mean reciprocal rank over a
random set of queries will be strongly influenced by the images of that user
to the detriment of the users who only contributed with few pictures. This is
a problem for several reasons: (i) the persons who produce the more data are
8 Pierre Bonnet et al.
usually the most expert ones but not the most representative of the potential
users of the automatic identification tools. (ii) The large number of the images
they produce makes the classification of their observations easier because they
tend to follow the same protocol for all their observations (same device, same
position of the plant in the images, etc.) (iii) The images they produce are
also usually of better quality so that their classification is even easier.
2.3 Quality-wise results
A total of 10 participating groups submitted 27 runs that are summarised
in the overview working note of the task [19] and further developed in the
individual working notes of the participants who submitted one (BME TMIT
[22], FINKI [28], I3S [31], IBM AU [26], IV-Processing [30], MIRACL [23],
PlantNet [24], QUT [25], Sabanki-Okan [27], SZTE [29]). Official results of
the evaluation (based on the score S detailed above) are synthesized in the
overview paper of LifeCLEF lab [32] and further developed in the working
note of the plant task [19]. For the purpose of this paper, we refined the
analysis of the collected runs in order to assess the impact of the quality of
the test pictures on the identification performances of the evaluated systems.
We therefore split the test observations in five categories according to the
rounded average number of stars of the images composing it. We remind that
the number of stars attributed to each image is itself an average number of the
quality ratings provided by the members of Tela Botanica social network and
supposed to reflect the interest of its visual content in terms of identification.
We then re-computed a per-category score S for each run and each quality
category. These new results are reported on Figure 4 and ?? for the 15 best runs
of the challenge. Note that the initial ranking of the runs has been preserved
in accordance to the overall official score of each run. This allows analysing if
the per-category rankings of the methods differ from that global one (and/or
between each other). Besides, the graph allows checking whether the overall
performance of a given run is stable or strongly affected by the query image
quality.
A first overall conclusion is that the performances of all runs degrade with
the average quality of the observations. The scores achieved on high-quality
pictures (with 4 or 5 stars) are two or three times the ones achieved on low-
quality pictures (with 1 or 2 stars). Whatever the underlying methods (e.g.
Fisher vectors and support vector machines, deep convolutional neural net-
works, local features and instance-based classifier, etc.), the impact of the
quality is high in all runs. This confirms that the human ratings accorded
to the visual content of the pictures are very well correlated with the ability
of the algorithms to recognize the captured species. Note that this was not
obvious for several reasons including the fact that some categories are more
populated than others in the dataset or that some teams could have imple-
mented specific quality-aware training strategies. The interesting point of the
sensitivity to image quality is that the performances on high-quality pictures
Plant identification: Man vs. Machine 9
Fig. 4 Identification performances of the 15 best runs detailed by quality categories
are also consistently better than the average performances. That means that it
is highly beneficial to teach potential users to take good pictures if they would
like to maximize their chance to identify a given plant observation. Note that
this is not a matter of taking professional pictures with expensive devices but
rather a matter of respecting a simple protocol and taking a few pictures until
one is good enough. All pictures in the 1 and 2 stars categories do for instance
not follow the basic instructions provided within the citizen science projects
launched by Pl@ntNet [5] (i.e. having one single part of the plant roughly
centered in the image). Pictures in the 1 star category are usually so bad that
any user understands that he cannot expect good results from it.
Now, looking into the details, we can see that all methods are not equally
affected by the image quality. We can first observe that the overall ranking of
the runs is modified across the categories. For instance the IBM AU Run 1,
the 3 runs of the BME TMIT team and the QUT Run 1 obtained better scores
than the PlantNet 2, PlantNet 4 and PlantNet 1 runs for the highest quality
category 5, while the overall ranking of the runs is more or less preserved for
the lowest quality categories 1, 2 & 3. The QUT Run 1 reach the 4th best score
for the quality category 4. This means that the methods used by BME TMIT,
QUT and IBM AU in these runs are more sensitive to the quality of the visual
content of the pictures, especially for the QUT Run 1 were the differences
between the 5 categories are amplified. This is also the case for the 3 best runs
4, 3 and 2 from the IBM AU were the performances reached impressive scores
over 0.6 on the quality categories 4 & 5, 0.657 being the highest score obtained
by IBM AU Run 4 on the quality category 4. It is interesting to notice that the
scores of IBM AU Run 4 on the lowest quality categories 1 & 2 is competitive,
even better, than the scores obtained on the highest quality categories 4 & 5
10 Pierre Bonnet et al.
obtained by half of the submitted runs, which is highlighting the relevance of
the approach developed by IBM AU (now acting as the new state of the art
in plant identification).
3 Man vs. Machine experiment
3.1 Evaluation protocol
To make the benchmark reachable for human expertise we first drastically re-
duced the number of observations to be identified. From the 8163 observations
in the test set of PlantCLEF we actually only kept 100 of them so that a human
run typically took between 45 minutes and 2 hours, according to the person’s
level of expertise. The selection of these 100 queries was done by iteratively
selecting 100 random observations from the whole dataset until the scores
obtained by the LifeCLEF runs on that subset respect the ranking observed
in the complete benchmark. This reduced test set was then shared with a
large audience of potential volunteers composed of three target groups: skilled
people (botanists, naturalists, teachers, more or less expert of the specifically
targeted flora), amateurs (people interested by plants in parallel of their pro-
fessional activity and having a knowledge of them at different expertise levels),
novices (inexperienced users). From the hundreds of contacted people, 20 of
them submitted a run (8 from the expert group, 7 from the amateur/student
group, 5 from the novice group). Note that the novice group has been the less
responding one mainly because of the higher difficulty and hardship for them
to complete the task.
The human runs themselves were collected through a user interface presenting
the 100 observations one by one (with one or several pictures of the different
organs) and allowing the user to select up to three species for each observa-
tion thanks to a drop-down menu covering the 500 species of the PlantCLEF
dataset. If the user provides no answer for a given query observation, the rank
of the correct species is considered as infinite in the evaluation metric ( 1
ru,p=0
in Equation 1). considered as infinite in the evaluation metric (formula 1). Note
that to allow a fair comparison with the machines we also limited the machine
runs to the same 100 observations and to the top-3 answers before comput-
ing the score S. It was also decided to display to the user the most popular
common names of each of the 500 species in addition to the scientific name of
the taxon, in order to facilitate participation of amateurs and novices. Date
and location of botanical observations were also provided in each file name.
As such information were also present in the xml files of the training and test
observations, the machine runs could also have used it and this does not affect
the fairness of the comparison.
A discussion has been conducted on whether we should provide pictures il-
lustrating the species to be identified and/or or authorize the use of external
resources such as field guides or the world wide web. A first positive argu-
ment was that the machines make use of the images in the training set so
Plant identification: Man vs. Machine 11
Subject expertise Avg nb of selected species Avg nb of empty responses
Novices 1,27 70,6
Amateurs 1,52 38,57
Experts 1,28 13,13
Table 2 Statistics on the number of species selected by the 3 categories of subject – the
average number of selected species is computed only on the non empty answers
Fig. 5 Identification performances between humans (green) and machines (red), the y-axis
is the metric defined in Equation (1).
that it would be fair to authorize humans to do so as well. But on the other
side, this could favor the brute-force approach consisting in scanning all the
species in the dataset one by one for each query which does not correspond to
any real usage and which would have taken days for conscientious people. We
therefore preferred restricting the evaluation to the knowledge-based identifi-
cation of plants, without any additional information or tools during the test.
As for the machines, people were thus supposed to have been trained before-
hand. Concerning the use of external sources of information (such as data from
wikipedia, eol6or ImageNET7), we remind that it was also forbidden for the
machine runs of the LifeCLEF campaign so that the comparison appeared to
be fair enough in that way.
6http://eol.org/
7http://www.image-net.org/
12 Pierre Bonnet et al.
3.2 Results analysis
Table 3.1 first presents some statistics about the average number of species
selected by the 3 categories of subjects. It shows that the average number of
empty responses is clearly correlated with the degree of expertise. This means
that the task is most of the time considered too hard by the subjects of the
novice group so that they even don’t try to provide an answer. Yet, when they
decide to provide an answer to a given query, the average number of species
they selected is roughly the same than the expert group. Only the amateur
group tends to select more species probably because they only have a rough
idea of what the plant is or is not.
Now, the identification results of the Man vs. Machine experiment are pro-
vided within Figure 5 with the machine runs in red (on the same subset of
100 queries) and the human runs in 3 different shades of green depending on
the user’s category (skilled, amateur or novice). First of all we can clearly
note that no human was able to correctly identify all botanical observations
even if a few of them are among the best known experts on French flora.
This means that even for specialists themselves, the correct identification of
botanical observation based on a few number of images is not always possi-
ble. The provided observations are actually most of the time incomplete for
an accurate identification that would require more pictures of other parts of
the plant. Also, botanical expertise is often organised by geographical zones
or taxonomical groups. It is, in fact, extremely difficult to develop a strong
expertise on large area and in the same time for a wide diversity of species.
Our benchmark with species belonging to 91 plant families and distributed on
the whole French territory is probably already too large to be fully succeeded
by an expert. Furthermore, for the same species, plant morphology can vary
a lot according to its growing stage, environmental condition or sexuality (for
flowering plants, male and female flowers can be very different, in size, color
or structure). Then, even if an expert knows the possible variations of a plant
morphology according to his field experience or the literature, numerous in-
formation about this variability may not yet be recorded and shared through
the scientific community, which can explain the difficulty to identify a species
in all conditions.
Now, the main outcome of the evaluation is that the best human runs still
clearly outperform all machine runs. All skilled people and several amateurs
did correctly identify more observations than the best system of IBM. The
worse performing systems have comparable performances with novices and in-
experienced users which limit their practical interest. These conclusions can
however be mitigated by a deeper analysis of the results with regard to the
image quality. We therefore re-computed the per-quality projection of the hu-
man vs. machines runs as we did before for the official benchmark (see section
2.3). Figure 6 first displays the per-quality score of each of the human run. We
can see that contrary to what we obtained on Figure 4 for the machine runs,
human runs are much less affected by the quality of the test pictures. It is even
hard to find any stable correlation between the identification performances and
Plant identification: Man vs. Machine 13
Fig. 6 Identification performances of the human runs detailed by quality categories
the quality of the observations. Very high-quality pictures with 5 stars ratings
often lead to worse performance than 4, 3 or even 2 stars pictures for some
users (independently from their expertise). This trend is confirmed by Figure
8 which displays the average identification performances across all human runs
and all machine runs for the different categories of image quality. To further
analyse the impact of image quality, Figure 7 displays the performances of
both the machine and the human runs but restricted to the test observations
with 3, 4 or 5 stars. It shows that the performances of the state-of-the-art
systems are much closer to the human expertise if we do not consider the
pictures with the lowest quality. IBM best run (based on Fisher vectors and
support vector machines) notably provides competitive performances with ex-
pert botanists in that case. Most runs also provide much better performances
than inexperienced users. Overall, that means that automated identification
systems would be already functional for many users who are ready to spend a
little time taking good pictures.
The results of our man vs. machine experiment finally have to be discussed
with regard to the user perception of the evaluated identification approaches.
Many of the test subjects, and especially the least expert in botany, did in
particular complain about the hardship of the quiz itself. As they often did
not know the name of the right species at the first glance, they sometimes tried
to scan the 500 possible species and proceed by elimination before choosing
one at random among the remaining candidates. This confirms the need of
combining both automatic recognition algorithms and powerful interactive in-
formation retrieval mechanisms. As proved by a user study reported in [5], the
14 Pierre Bonnet et al.
Fig. 7 Identification performances of the 15 best machine runs and 20 human runs restricted
to high-quality pictures (with 3, 4 or 5 stars).
Fig. 8 Average identification performances across all human runs and all machine runs for
the different categories of image quality.
raw performances of automatic identification systems can actually be strongly
boosted once integrated in a real interactive application allowing the user to
navigate the top results and visually check the proposed species. A perspec-
tive of the experiment reported in that paper could therefore be to complete
Plant identification: Man vs. Machine 15
it with man+machine runs highlighting their mutual benefit.
4 Conclusion and perspectives
On May 11, 1997, Deep Blue, a chess-playing computer developed by IBM
won the second six-game match against world champion Garry Kasparov, two
to one, with three draws (with human intervention between games). At the
image-based plant identification competition however, machines are still far
from outperforming the best expert botanists. Our study shows that all skilled
subjects with a minimal expertise in botany achieve better identification per-
formances than the best systems evaluated within LifeCLEF 2014 (even if they
were not expert of the specific flora addressed in the benchmark). We notably
did show that the machine predictions are much more affected by the quality
of the test images meaning that the underlying computer vision algorithms
are still much more sensitive to the acquisition conditions than the human
visual system. On the other side, the performances of the best systems are
more than promising for the future. IBM best run (based on Fisher vectors
and support vector machines) provides competitive performances with expert
botanists on high-quality pictures. Several runs also provide much better per-
formances than inexperienced users when restricting the evaluation to images
with 3, 4 and 5 stars ratings. That means that semi-automated identification
systems are already functional for users who are ready to spend a little time
taking good pictures. Finally, it is important to remind that the use of external
data was not authorized for training the systems evaluated in LifeCLEF. It
is for instance worth noting that deep artificial neural networks could provide
much better performances if they were pre-trained on large external corpora
(even not related to botany). For the upcoming LifeCLEF evaluations, we will
study the feasibility of providing more species as well as the feasibility of au-
thorizing any other external training data to further improve the reliability of
our challenge.
Acknowledgements Part of this work was funded by the Agropolis foundation through
the project Pl@ntNet (http://www.plantnet-project.org/).
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