Crowdsourcing Biodiversity Monitoring: How Sharing your Photo Stream can Sustain our Planet

International audienc

TasselNet: Counting maize tassels in the wild via local counts regression network

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.Comment: 14 page

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

Deep Learning Based Fine Grained Image Classification

Image classification, specifically object classification is the focused research area in the computer vision and machine learning field in the past decade. In image classification a label or category is assigned to an input image based on its content. With breakthroughs in deep learning-based approaches, performance of image classification models' has improved significantly, particularly fine-grained image classification, which includes discriminating between items of the same category with slight changes. The object classification can be categorised as coarse grained object classification, which identifies highly diverse object categories, such as an elephant and a bus. One example of this type of object classification is a bus and an elephant. On the other hand, fine-grained image categorization seeks to recognise photos as belonging to distinct species of animals, birds, or plants, as well as distinct models of automobiles, versions of aircraft, and so on. The purpose of this study is to evaluate previously published research that investigates deep learning techniques for the classification of fine-grained images and to compare the effectiveness of these techniques using datasets that are open to the public

Ecosystem properties and principles of living systems as foundation for sustainable agriculture – Critical reviews of environmental assessment tools, key findings and questions from a course process

With increasing demands on limited resources worldwide, there is a growing interest in sustainable patterns of utilisation and production. Ecological agriculture is a response to these concerns. To assess progress and compliance, standard and comprehensive measures of resource requirements, impacts and agro-ecological health are needed. Assessment tools should also be rapid, standardized, userfriendly, meaningful to public policy and applicable to management. Fully considering these requirements confounds the development of integrated methods. Currently, there are many methodologies for monitoring performance, each with its own foundations, assumptions, goals, and outcomes, dependent upon agency agenda or academic orientation. Clearly, a concept of sustainability must address biophysical, ecological, economic, and sociocultural foundations. Assessment indicators and criteria, however, are generally limited, lacking integration, and at times in conflict with one another. A result is that certification criteria, indicators, and assessment methods are not based on a consistent, underlying conceptual framework and often lack a management focus. Ecosystem properties and principles of living systems, including self-organisation, renewal, embeddedness, emergence and commensurate response provide foundation for sustainability assessments and may be appropriate focal points for critical thinking in an evaluation of current methods and standards. A systems framework may also help facilitate a comprehensive approach and promote a context for meaningful discourse. Without holistic accounts, sustainable progress remains an illdefined concept and an elusive goal. Our intent, in the work with this report, was to use systems ecology as a pedagogic basis for learning and discussion to: - Articulate general and common characteristics of living systems. - Identify principles, properties and patterns inherent in natural ecosystems. - Use these findings as foci in a dialogue about attributes of sustainability to: a. develop a model for communicating scientific rationale. b. critically evaluate environmental assessment tools for application in land-use. c. propose appropriate criteria for a comprehensive assessment and expanded definition of ecological land use

AI naturalists might hold the key to unlocking biodiversity data in social media imagery

The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword “flower” across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis

Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities