Affouard, Antoine

Bonnet, Pierre

Goëau, Hervé

Joly, Alexis

Lombardo, Jean-Christophe

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Pl@ntNet app in the era  of deep learning

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HAL Id: hal-01629195https://hal.archives-ouvertes.fr/hal-01629195Submitted on 6 Nov 2017HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Pl@ntNet app in the era of deep learningAntoine Affouard, Hervé Goëau, Pierre Bonnet, Jean-Christophe Lombardo,Alexis JolyTo cite this version:Antoine Affouard, Hervé Goëau, Pierre Bonnet, Jean-Christophe Lombardo, Alexis Joly. Pl@ntNetapp in the era of deep learning. ICLR: International Conference on Learning Representations, Apr2017, Toulon, France. ￿hal-01629195￿Workshop track - ICLR 2017PL@NTNET APP IN THE ERA OF DEEP LEARNINGAntoine Affouard, Hervé Goeau, Pierre BonnetPl@ntNet project, AMAP joint research unit, France{antoine.affouard,herve.goeau,pierre.bonnet}@cirad.frJean-Christophe Lombardo, Alexis JolyPl@ntNet project, Inria, ZENITH team, France{jean-christophe.lombardo,alexis.joly}@inria.frABSTRACTPl@ntNet is a large-scale participatory platform and information system dedi-cated to the production of botanical data through image-based plant identification.In June 2015, Pl@ntNet mobile front-ends moved from classical hand-crafted vi-sual features to deep-learning based image representations. This paper gives anoverview of today’s Pl@ntNet architecture and discusses how the introduction ofconvolutional neural networks did improve the whole workflow along the years.1 INTRODUCTIONPl@ntNet is a large-scale participatory platform and information system dedicated to the productionof botanical data through image-based plant identification. It offers 3 main front-ends, an Androidapp (the most advanced and the most used one), an iOs app (being currently re-developed) anda web interface1, each allowing to submit one or several pictures of a plant in order to get a listof the most likely species in return. As illustrated by Figure 2 in the appendix, the application isbecoming more and more popular especially during spring and summer. Nowadays, the applicationis translated in eleven languages and has been downloaded by more than 3 millions users in about170 countries (it is often referred to as the shazam of plants). One of the main strength of Pl@ntNetis that the training set enabling the automated recognition is collaboratively enriched and revised.Indeed, users can share their observation with the community (whether they have identified it ornot). Several collaborative tools are then available for data validation:• a web platform called IdentiPlante2 that is hosted by one of the largest network of amateurand expert botanists in the world (TelaBotanica, about 30K French speaking members).• A gamified web application called ThePlantGame3 that is based on a set of data-drivenalgorithms allowing to (i) actively train annotators, and (ii) evaluate the quality of contrib-utors answers on new test items in order to optimize predictions.• embedded validation mechanisms in the Android and Web front-ends themselvesAll these validation tools allow the app to cover a growing number of floras and species. It wasinitially restricted to a fraction of the European flora (in 2013) and has then been extended to theIndian ocean flora and south American one (in 2015), and last year to the north African flora (2016).Numbers of species covered by the app in January 2017 are provided in Table 4 of the appendix.2 PL@NTNET ARCHITECTUREFigure 1 presents an overview of Pl@ntNet architecture (at the beginning of 2017). We give hereaftersome details of the main modules:1identify.plantnet-project.org2www.tela-botanica.org/appli:identiplante3www.theplantgame.com1Workshop track - ICLR 2017Plant Observations Storage: Plant observations are the basic data unit of Pl@ntNet informationsystem. An observation documents evidence of a living individual plant. It includes one or severalimages of the plant tagged with a view type (flower, leaf, fruit, stem or entire plant) as well assome provenance data (device, author, date, etc.). Each observation is optionally associated to ageo-location and to one or more determinations, i.e. possible species names proposed by the authorhimself, and/or by other annotators, and/or by automated classifiers. A determination is tagged asvalid when its confidence score (computed through different inference algorithms) exceeds somethreshold. Observations are stored whithin a NoSQL document storage called CouchDb.Image representation learning and extraction: Image representations are computed by a convolu-tional neural network (CNN) that is periodically trained in a supervised manner on the observationswith a valid determination name and an additional rejection class (containing non-plant picturestaken by Pl@ntNet users, e.g. faces, animals, manufactured objects, etc.). At the time of writing,the used CNN architecture is the inception model Szegedy et al. (2015) extended with batch normal-ization Ioffe & Szegedy (2015). The network is pre-trained on ImageNet dataset Deng et al. (2009)and periodically fine-tuned on Pl@ntNet data. The number of species (i.e. classes) in January 2017was about 10K and the number of training images about 332K.Species prediction: As an observation might be composed of several pictures of the observed plant,the predictions of the CNN for each searched picture need to be fused. We therefore use a weightedaverage of the SOFTMAX probability vectors. The weight of each picture only depends on its viewtype (e.g. the pictures tagged as flower are more weighted than the pictures tagged as leaf becauseflowers are much more discriminant than leaves). The values of the weights have been optimizedempirically. After this fusion process, a species filtering is applied based on the checklist of speciesactivated within the app (e.g. West Europe, North Africa, South America, etc.). By default, thischecklist is automatically chosen according to the geo-location of the mobile device, but the useralso has the possibility to select another one manually. Note that all the checklists share somecommon species. The degree of overlap depends on environmental the factors of the covered areas.Similarity Search: In addition to the most probable species, Pl@ntNet’s search engine returns theimages of the dataset that are the most similar to the queried observation. This allows improving theinteractive identification process by illustrating the candidate species with images consistent withthe observed individual plant (the variability within the same species can indeed be high as well asthe shooting protocol). This content-based image retrieval is performed through a hashing-basedapproximate nearest neighbors search algorithm applied on top of the 1024-dimensional featurevectors extracted by the last hidden layer of the fine-tuned CNN. The feature vector of each imageis compressed into a compact binary code thanks to an unsupervised hashing method (RMMH: Joly& Buisson (2011)) and its approximate k-nearest neighbors are searched by probing neighboringbuckets in the hash table (using the multi-probe algorithm described in Joly & Buisson (2008)).Note that a separate hash table is used for each checklist to focus the search on the right species andfacilitate the distribution of the whole index. If a candidate species does not have any representativeimage in the returned top-k, it is not displayed to the user (actually, this adds a distance rejectioncriterion to the recognition process).Web API and front-ends: Data exchanges between the search engine (server side) and the front-ends (client side) are managed through a REST-full web API using json data format and jpeg images.The list of candidate species predicted for a searched plant observation is displayed in decreasingorder of confidence and each species is illustrated by the most similar picture of that species. Ifthe user selects a species, the other retrieved images are displayed by decreasing similarity scores(grouped by type of view). At this stage, the user can access to richer information about the speciessuch as all the available illustrations in the dataset (allowing to refine the identification) or descriptiveweb pages of the species providing useful information about the plant.3 THE CONTRIBUTION OF IMAGE REPRESENTATIONS LEARNINGPl@ntNet’s visual search engine started working with learned image representations in June 2015(thanks to the integration of CNNs). Before that date, both the species prediction and the similaritysearch were based on a combination of hand-crafted visual features as described in Goëau et al.2Workshop track - ICLR 2017(2014). We provide hereafter three evaluation dimensions showing the benefit of having moved tolearned representations.Figure 1: Pl@ntNet system architectureRecognition performance improvementTable 4 in the appendix gives an overview of the identification performance of Pl@ntNet systemalong the years, as measured within the yearly system-oriented evaluation campaign LifeCLEF4 (seereferences in the table for details about the metric, dataset, evaluation protocol and overall results ofeach year). The table shows that, despite the increasing difficulty of the task (more species, morevariety and more noise along the years), a huge performance gap was observed in 2015 with theintroduction of CNN’s. This gap was also observed for other regular participants and, in 2016, 14 ofthe 15 participants were using deep learning in their system as described in Goëau et al. (2016).User’s perceptionFigure 3 in the appendix gives an overview of Pl@ntNet user’s ratings collected through the dig-ital media store Google Play store (where the Android front-end is distributed). It shows that themonthly average rating gradually increased from about 3.2 to about 4.1 after the deep-learning basedimage representations were integrated in June 2015. It is likely that other improvements of the ap-plication also contributed to a better perception (actually, the ratings were already increasing be-forehand). But the integration of the CNN clearly accelerated the increase and is one of the mainexplanatory factor.Qualitative evidencesFigure 4 in the appendix provides some qualitative evidences of the benefit of using CNN-basedimage representations within Pl@ntNet application. For 4 observations made in March 2015, itprovides the results returned by the mobile app at that time (using on hand-crafted visual features)compared to the results returned today (using the architecture described in section 2). It shows thatbeyond the raw increase of the identification performance, the search engine now returns resultsthat are much more consistent with the query image. Even for the wrong species predictions, thereturned similar images share easily interpretable visual patterns with the observed plant and theyoften correspond to species with known confusion with the true one. In practice, this makes theapplication a much more user-friendly and pedagogical tool.4 CONCLUSIONThis paper describes for the time the architecture of the Pl@ntNet information system since con-volutional neural networks were introduced in June 2015. It illustrates in what way moving fromhand-crafted image representations to the use of deep learning in an information system can have aconsistent societal impact. Today, Pl@ntNet is largely used in various professional contexts (agri-culture, education, ecotourism, etc.). It contributes to develop an increasing interest of a large part ofthe society to their environment as well as new forms of educational trainings in botany and ecology.4www.lifeclef.org3Workshop track - ICLR 2017REFERENCESVera Bakic, Itheri Yahiaoui, Sofiene Mouine, Saloua Litayem Ouertani, Wajih Ouertani, Anne Verroust-Blondet, Hervé Goëau, and Alexis Joly. Inria imedia2’s participation at imageclef 2012 plant identificationtask. In CLEF working notes, 2012.Vera Bakic, Sofiene Mouine, Saloua Ouertani-Litayem, Anne Verroust-Blondet, Itheri Yahiaoui, Hervé Goëau,and Alexis Joly. Inria’s participation at imageclef 2013 plant identification task. In CLEF working notes,2013.Julien Champ, Titouan Lorieul, Maximilien Servajean, and Alexis Joly. A comparative study of fine-grainedclassification methods in the context of the lifeclef plant identification challenge 2015. In CLEF workingnotes, volume 1391, 2015.Julien Champ, Hervé Goëau, and Alexis Joly. Floristic participation at lifeclef 2016 plant identification task.In CLEF working notes, pp. 450–458, 2016.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchicalimage database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp.248–255. IEEE, 2009.Hervé Goëau, Alexis Joly, Itheri Yahiaoui, Vera Bakić, Anne Verroust-Blondet, Pierre Bonnet, DanielBarthélémy, Nozha Boujemaa, and Jean-François Molino. Plantnet participation at lifeclef2014 plant iden-tification task. In CLEF2014 Working Notes. Working Notes for CLEF 2014 Conference, Sheffield, UK,September 15-18, 2014, pp. 724–737. CEUR-WS, 2014.Hervé Goëau, Pierre Bonnet, and Alexis Joly. Plant identification in an open-world (lifeclef 2016). CLEFworking notes, 2016, 2016.Joly A. Yahiaoui I. Bonnet P. Mouysset E. Goëau, H. Participation of inria pl@ntnet to imageclef 2011 plantimages classification task. In CLEF working notes, 2011.Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducinginternal covariate shift. arXiv preprint arXiv:1502.03167, 2015.Alexis Joly and Olivier Buisson. A posteriori multi-probe locality sensitive hashing. In Proceedings of the 16thACM international conference on Multimedia, pp. 209–218. ACM, 2008.Alexis Joly and Olivier Buisson. Random maximum margin hashing. In Computer Vision and Pattern Recog-nition (CVPR), 2011 IEEE Conference on, pp. 873–880. IEEE, 2011.Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan,Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, pp. 1–9, 2015.4Workshop track - ICLR 2017APPENDIXheight Floras #Species cover by Pl@ntNet #Flora species #PercentageWestern Europe 6220 7268 85.58Indian Ocean 1204 2541 47.38French Guyana 928 8047 11.53North Africa 2742 8305 33.01Table 1: Synthesis of the number of species covered by Pl@ntNetYears #Species #Images Score (mAP) Used image representations Reference2011 70 5,436 0.197 Hand-crafted local features Goëau (2011)2012 126 11,572 0.22 Hand-crafted local features Bakic et al. (2012)2013 250 26,077 0,385 Hand-crafted local features Bakic et al. (2013)2014 500 60,961 0.289 Hand-crafted local features Goëau et al. (2014)2015 1,000 113,205 0.609 Learned through CNN Champ et al. (2015)2016 +1,000 121,2015 0.627 Learned through CNN Champ et al. (2016)Table 2: Synthesis of the performance of Pl@ntNet system at LifeCLEF plant challengeFigure 2: (a) Average daily number of users (b) Percentage of sessions per trimesterFigure 3: User’s ratings of Pl@ntNet android app5Workshop track - ICLR 2017Figure 4: Results returned by Pl@ntNet application before and after the introduction of CNN’s (thespecies in red corresponds to the true label of the query6

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Pl@ntNet app in the era of deep learning

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