A look inside the Pl@ntNet experience

International audiencePl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps

Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution

International audienceThis paper presents a novel image dataset with high intrinsic ambiguity and a longtailed distribution built from the database of Pl@ntNet citizen observatory. It consists of 306,146 plant images covering 1,081 species. We highlight two particular features of the dataset, inherent to the way the images are acquired and to the intrinsic diversity of plants morphology: (i) the dataset has a strong class imbalance, i.e., a few species account for most of the images, and, (ii) many species are visually similar, rendering identification difficult even for the expert eye. These two characteristics make the present dataset well suited for the evaluation of set-valued classification methods and algorithms. Therefore, we recommend two set-valued evaluation metrics associated with the dataset (macro-average top-k accuracy and macro-average average-k accuracy) and we provide baseline results established by training deep neural networks using the cross-entropy loss

DataManager, un système novateur de gestion et d’échange de données botaniques distribuées

National audienceDataManager, un système novateur de gestion et d'échange de données botaniques distribuées STRUCTURATION DES DONNÉES Cette application web est dédiée à des scientifiques souhaitant gérer des jeux de données spécifiques, avec le souhait de partager une partie de leur travail. Pl@ntNet-DataManager est développé avec un moteur de base de données NoSQL, offrant des fonctionnalités innovantes, notamment pour une structuration flexible des données, ainsi que des fonctions avancées de synchronisation. Ce système offre des fonctionnalités classiques de gestion de données, telles que la recherche libre, l'édition de requêtes structurées, l'import / export à différents formas, la gestion d'images ou de données géo-localisées. Ce travail a permis d'initier une nouvelle forme de gestion de gros volumes de données. Il se poursuit actuellement à travers son exploitation dans le cadre de la chaîne logicielle Pl@ntNet, notamment pour la gestion d'observations botaniques et des données visuelles associée

Pl@ntNet app in the era of deep learning

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Pl@ntNet -My Business

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Pl@ntNet

Pl@ntNet is a participatory platform and information system dedicated to the production of botanical data through deep learning-based plant identification. It includes 3 main front-ends, an Android app (the most advanced and the most used one), an iOs app (being currently re-developed) and a web version. The main feature of the application is to return the ranked list of the most likely species providing an image or an image set of an individual plant. In addition, Pl@ntNet’s search engine returns the images of the dataset that are the most similar to the queried observation allowing interactive validation by the users. The back-office running on the server side of the platform is based on Snoop visual search engine (a software developed by ZENITH) and on NewSQL technologies for the data management. The application is distributed in more than 180 countries (10M downloads) and allows identifying about 20K plant species at present time

Cooperative learning of Pl@ntNet’s Artificial Intelligence algorithm using label aggregation

National audienceThe Pl@ntNet system enables global data collection by allowing users to upload and annotate plant observations, leading to noisy labels due to diverse user skills.Achieving consensus is crucial for training, but the vast scale of collected data makes traditional label aggregation strategies challenging. Additionally, as many species are rarely observed, user expertise can not be evaluated as an inter-user agreement: otherwise, botanical experts would have a lower weight in the training step than the average user as they have fewer but precise participation. Our proposed label aggregation strategy aims to cooperatively train plant identification models. This strategy estimates user expertise as a trust score per worker based on their ability to identify plant species from crowdsourced data.The trust score is recursively estimated from correctly identified species given the currentestimated labels. This interpretable score exploits botanical experts’ knowledge and the heterogeneity of users. We evaluate our strategy on a large subset of the Pl@ntNet database focused on European flora, comprising over 6 000 000 observations and 800 000 users.We demonstrate that estimating users’ skills based on the diversity of their expertise enhanceslabeling performance

Pl@ntNet

Pl@ntNet is a participatory platform and information system dedicated to the production of botanical data through deep learning-based plant identification. It includes 3 main front-ends, an Android app (the most advanced and the most used one), an iOs app (being currently re-developed) and a web version. The main feature of the application is to return the ranked list of the most likely species providing an image or an image set of an individual plant. In addition, Pl@ntNet’s search engine returns the images of the dataset that are the most similar to the queried observation allowing interactive validation by the users. The back-office running on the server side of the platform is based on Snoop visual search engine (a software developed by ZENITH) and on NewSQL technologies for the data management. The application is distributed in more than 180 countries (10M downloads) and allows identifying about 20K plant species at present time