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

Next-Generation Field Guides

To conserve species, we must first identify them. Field researchers, land managers, educators, and citizen scientists need up-to-date and accessible tools to identify organisms, organize data, and share observations. Emerging technologies complement traditional, book-form field guides by providing users with a wealth of multimedia data. We review technical innovations of next-generation field guides, including Web-based and stand-alone applications, interactive multiple-access keys, visual-recognition software adapted to identify organisms, species checklists that can be customized to particular sites, online communities in which people share species observations, and the use of crowdsourced data to refine machine-based identification algorithms. Next-generation field guides are user friendly; permit quality control and the revision of data; are scalable to accommodate burgeoning data; protect content and privacy while allowing broad public access; and are adaptable to ever-changing platforms and browsers. These tools have great potential to engage new audiences while fostering rigorous science and an appreciation for nature.Organismic and Evolutionary Biolog

Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop

Existing fine-grained visual categorization methods often suffer from three challenges: lack of training data, large number of fine-grained categories, and high intraclass vs. low inter-class variance. In this work we propose a generic iterative framework for fine-grained categorization and dataset bootstrapping that handles these three challenges. Using deep metric learning with humans in the loop, we learn a low dimensional feature embedding with anchor points on manifolds for each category. These anchor points capture intra-class variances and remain discriminative between classes. In each round, images with high confidence scores from our model are sent to humans for labeling. By comparing with exemplar images, labelers mark each candidate image as either a "true positive" or a "false positive". True positives are added into our current dataset and false positives are regarded as "hard negatives" for our metric learning model. Then the model is retrained with an expanded dataset and hard negatives for the next round. To demonstrate the effectiveness of the proposed framework, we bootstrap a fine-grained flower dataset with 620 categories from Instagram images. The proposed deep metric learning scheme is evaluated on both our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show significant performance gain using dataset bootstrapping and demonstrate state-of-the-art results achieved by the proposed deep metric learning methods.Comment: 10 pages, 9 figures, CVPR 201

Plant Identification in an Open-world (LifeCLEF 2016)

International audienceThe LifeCLEF plant identification challenge aims at evaluating plant identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the identification task was evaluated as an open-setrecognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes

The ImageCLEF 2013 Plant Identification Task

International audienceThe ImageCLEF's plant identification task provides a testbed for a system-oriented evaluation of plant identification about 250 species trees and herbaceous plants based on detailed views of leaves, flowers, fruits, stems and bark or some entire views of the plants. Two types of image content are considered: SheetAsBackgroud which contains only leaves in a front of a generally white uniform background, and NaturalBackground which contains the 5 kinds of detailed views with unconstrained conditions, directly photographed on the plant. The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of twelve groups from nine countries and with a total of thirty three runs submitted, involving distinct and original methods, this third year task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification

Overcoming biodiversity blindness: Secondary data in primary citizen science observations

1. In the face of the global biodiversity crisis, collecting comprehensive data and making the best use of existing data are becoming increasingly important to understand patterns and drivers of environmental and biological phenomena at different scales. 2. Here we address the concept of secondary data, which refers to additional information unintentionally captured in species records, especially in multimedia-based citizen science reports. We argue that secondary data can provide a wealth of ecologically relevant information, the utilisation of which can enhance our understanding of traits and interactions among individual organisms, populations and biodiversity dynamics in general. 3. We explore the possibilities offered by secondary data and describe their main types and sources. An overview of research in this field provides a synthesis of the results already achieved using secondary data and different approaches to information extraction. 4. Finally, we discuss challenges to the widespread use of secondary data, such as biases, licensing issues, use of metadata and lack of awareness of this trove of data due to a missing common terminology, as well as possible solutions to overcome these barriers. 5. Although the exploration and use of secondary data is only emerging, the many opportunities identified show how these data can enrich biodiversity research and monitoring

Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region: Report of training workshop, Addis Ababa Ethiopia, 17-21 September 2019

Bioversity International is implementing a Dutch-supported project entitled: Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region. This work aims to boost timely and affordable access to good-quality seed for a portfolio of crops / varieties for millions of women and men farmers’ and their communities across east Africa. A first project training: i) contextualized farmer varietal selection, ii) provided practical demonstrations of tools for climate-change analysis, iii) introduced policy issues associated with managing crop diversity, iv) outlined characterization and evaluation of genetic resources, and v) articulated associated gender issues, and issues related to disseminating elite materials. The training concluded with a contextualizing field trip. In the workshop evaluation, 98% participants declared their overall satisfaction level to be high (74%) or medium (24%), indicating the training furnished them with good ideas for networking and using the tools and methods they learned about

LifeCLEF 2016: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcom

The iNaturalist Species Classification and Detection Dataset

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.Comment: CVPR 201