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Mesaglio T, Sauquet H, Coleman D, Wenk E, Cornwell WK. Photographs as an essential biodiversity resource: drivers of gaps in the vascular plant photographic record. THE NEW PHYTOLOGIST 2023; 238:1685-1694. [PMID: 36913725 DOI: 10.1111/nph.18813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
The photographic record is increasingly becoming an important biodiversity resource for primary research and conservation monitoring. However, globally, there are important gaps in this record even in relatively well-researched floras. To quantify the gaps in the Australian native vascular plant photographic record, we systematically surveyed 33 sources of well-curated species photographs, assembling a list of species with accessible and verifiable photographs, as well as the species for which this search failed. Of 21 077 Australian native species, 3715 lack a verifiable photograph across our 33 surveyed resources. There are three major geographic hotspots of unphotographed species in Australia, all far from current population centres. Many unphotographed species are small in stature or uncharismatic, and many are also recently described. The large number of recently described species without accessible photographs was surprising. There are longstanding efforts in Australia to organise the plant photographic record, but in the absence of a global consensus to treat photographs as an essential biodiversity resource, this has not become common practice. Many recently described species are small-range endemics and some have special conservation status. Completing the botanical photographic record across the globe will facilitate a virtuous feedback loop of more efficient identification, monitoring and conservation.
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Affiliation(s)
- Thomas Mesaglio
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Hervé Sauquet
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
- National Herbarium of New South Wales, Royal Botanic Gardens and Domain Trust, Sydney, NSW, 2000, Australia
| | - David Coleman
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Elizabeth Wenk
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - William K Cornwell
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, 2052, Australia
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Geometric Morphometric Versus Genomic Patterns in a Large Polyploid Plant Species Complex. BIOLOGY 2023; 12:biology12030418. [PMID: 36979110 PMCID: PMC10045763 DOI: 10.3390/biology12030418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023]
Abstract
Plant species complexes represent a particularly interesting example of taxonomically complex groups (TCGs), linking hybridization, apomixis, and polyploidy with complex morphological patterns. In such TCGs, mosaic-like character combinations and conflicts of morphological data with molecular phylogenies present a major problem for species classification. Here, we used the large polyploid apomictic European Ranunculus auricomus complex to study relationships among five diploid sexual progenitor species and 75 polyploid apomictic derivate taxa, based on geometric morphometrics using 11,690 landmarked objects (basal and stem leaves, receptacles), genomic data (97,312 RAD-Seq loci, 48 phased target enrichment genes, 71 plastid regions) from 220 populations. We showed that (1) observed genomic clusters correspond to morphological groupings based on basal leaves and concatenated traits, and morphological groups were best resolved with RAD-Seq data; (2) described apomictic taxa usually overlap within trait morphospace except for those taxa at the space edges; (3) apomictic phenotypes are highly influenced by parental subgenome composition and to a lesser extent by climatic factors; and (4) allopolyploid apomictic taxa, compared to their sexual progenitor, resemble a mosaic of ecological and morphological intermediate to transgressive biotypes. The joint evaluation of phylogenomic, phenotypic, reproductive, and ecological data supports a revision of purely descriptive, subjective traditional morphological classifications.
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Small Leaves, Big Diversity: Citizen Science and Taxonomic Revision Triples Species Number in the Carnivorous Drosera microphylla Complex ( D. Section Ergaleium, Droseraceae). BIOLOGY 2023; 12:biology12010141. [PMID: 36671833 PMCID: PMC9855878 DOI: 10.3390/biology12010141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023]
Abstract
The carnivorous Drosera microphylla complex from southwest Western Australia comprises a group of rare, narrowly endemic species that are potentially threatened by habitat destruction and illegal collection, thus highlighting a need for accurate taxonomic classification to facilitate conservation efforts. Following extensive fieldwork over two decades, detailed studies of both Australian and European herbaria and consideration of both crucial contributions by citizen scientists and social media observations, nine species of the D. microphylla complex are here described and illustrated, including four new species: D. atrata, D. hortiorum, D. koikyennuruff, and D. reflexa. The identities of the previously described infraspecific taxa D. calycina var. minor and D. microphylla var. macropetala are clarified. Both are here lectotypified, reinstated, and elevated to species rank. A replacement name, D. rubricalyx, is provided for the former taxon. Key morphological characters distinguishing the species of this complex include the presence or absence of axillary leaves, lamina shape, petal colour, filament shape, and style length. A detailed identification key, comparison figures, and a distribution map are provided. Six of the nine species are recommended for inclusion on the Priority Flora List under the Conservation Codes for Western Australian Flora and Fauna.
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Wäldchen J, Wittich HC, Rzanny M, Fritz A, Mäder P. Towards more effective identification keys: A study of people identifying plant species characters. PEOPLE AND NATURE 2022. [DOI: 10.1002/pan3.10405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Jana Wäldchen
- Max Planck Institute for Biogeochemistry Jena Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Germany
| | | | | | - Alice Fritz
- Max Planck Institute for Biogeochemistry Jena Germany
| | - Patrick Mäder
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Germany
- Data Intensive Systems and Visualisation Technische Universität Ilmenau Ilmenau Germany
- Faculty of Biological Sciences Friedrich Schiller University Jena Germany
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A Performance Comparison of Classification Algorithms for Rose Plants. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1842547. [PMID: 36238676 PMCID: PMC9552688 DOI: 10.1155/2022/1842547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/27/2022] [Accepted: 07/16/2022] [Indexed: 11/17/2022]
Abstract
One of the key roles of Botanists is to be able to recognize flowers. This role has
become highly challenging given that the number of discovered flower types are nearing
half a million. To support Botanists, Information Technology offers promising solutions.
Specifically, machine learning techniques are intrinsically appealing due to being precise
enough as required. To this aim, two observations on flower leaves are relevant and
leverage flower identification: one, flower plants exhibit unique features in their leaves
thus allow distinction of their co-located flowers; two, leaves have a much longer life
than flowers thus preserve identity properties longer. This paper proposes the use of
machine learning-based identification of rose types by leveraging the features from their
leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model,
Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support
Vector Machine has been analyzed. This study optimizes the RF model by investigating and
tuning its various parameters such as the number of trees, the depth of trees, and
splitting criteria. The best results are achieved with gain ratio because it takes more
distinct values to avoid the problems associated with Information Gain. Optimizing the
number of trees and the depth of trees of RF yield better accuracy than other models.
Extensive experiments are performed to analyze the results of ensemble algorithms by using
the voting method for each instance. Results suggest that the performance of ensemble
classifiers is superior to that of individual models.
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Hofmann M, Mader P. Synaptic Scaling-An Artificial Neural Network Regularization Inspired by Nature. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3094-3108. [PMID: 33502984 DOI: 10.1109/tnnls.2021.3050422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Nature has always inspired the human spirit and scientists frequently developed new methods based on observations from nature. Recent advances in imaging and sensing technology allow fascinating insights into biological neural processes. With the objective of finding new strategies to enhance the learning capabilities of neural networks, we focus on a phenomenon that is closely related to learning tasks and neural stability in biological neural networks, called homeostatic plasticity. Among the theories that have been developed to describe homeostatic plasticity, synaptic scaling has been found to be the most mature and applicable. We systematically discuss previous studies on the synaptic scaling theory and how they could be applied to artificial neural networks. Therefore, we utilize information theory to analytically evaluate how mutual information is affected by synaptic scaling. Based on these analytic findings, we propose two flavors in which synaptic scaling can be applied in the training process of simple and complex, feedforward, and recurrent neural networks. We compare our approach with state-of-the-art regularization techniques on standard benchmarks. We found that the proposed method yields the lowest error in both regression and classification tasks compared to previous regularization approaches in our experiments across a wide range of network feedforward and recurrent topologies and data sets.
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Hussein BR, Malik OA, Ong WH, Slik JWF. Applications of computer vision and machine learning techniques for digitized herbarium specimens: A systematic literature review. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Spagnuolo EJ, Wilf P, Serre T. Decoding family-level features for modern and fossil leaves from computer-vision heat maps. AMERICAN JOURNAL OF BOTANY 2022; 109:768-788. [PMID: 35319778 DOI: 10.1002/ajb2.1842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 03/07/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
PREMISE Angiosperm leaves present a classic identification problem due to their morphological complexity. Computer-vision algorithms can identify diagnostic regions in images, and heat map outputs illustrate those regions for identification, providing novel insights through visual feedback. We investigate the potential of analyzing leaf heat maps to reveal novel, human-friendly botanical information with applications for extant- and fossil-leaf identification. METHODS We developed a manual scoring system for hotspot locations on published computer-vision heat maps of cleared leaves that showed diagnostic regions for family identification. Heat maps of 3114 cleared leaves of 930 genera in 14 angiosperm families were analyzed. The top-5 and top-1 hotspot regions of highest diagnostic value were scored for 21 leaf locations. The resulting data were viewed using box plots and analyzed using cluster and principal component analyses. We manually identified similar features in fossil leaves to informally demonstrate potential fossil applications. RESULTS The method successfully mapped machine strategy using standard botanical language, and distinctive patterns emerged for each family. Hotspots were concentrated on secondary veins (Salicaceae, Myrtaceae, Anacardiaceae), tooth apices (Betulaceae, Rosaceae), and on the little-studied margins of untoothed leaves (Rubiaceae, Annonaceae, Ericaceae). Similar features drove the results from multivariate analyses. The results echo many traditional observations, while also showing that most diagnostic leaf features remain undescribed. CONCLUSIONS Machine-derived heat maps that initially appear to be dominated by noise can be translated into human-interpretable knowledge, highlighting paths forward for botanists and paleobotanists to discover new diagnostic botanical characters.
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Affiliation(s)
- Edward J Spagnuolo
- Department of Geosciences and Earth and Environmental Systems Institute, Pennsylvania State University, University Park, Pennsylvania, 16802, USA
- Millennium Scholars Program, Pennsylvania State University, University Park, Pennsylvania, 16802, USA
- Schreyer Honors College, Pennsylvania State University, University Park, Pennsylvania, 16802, USA
| | - Peter Wilf
- Department of Geosciences and Earth and Environmental Systems Institute, Pennsylvania State University, University Park, Pennsylvania, 16802, USA
| | - Thomas Serre
- Department of Cognitive, Linguistic and Psychological Sciences, Carney Institute for Brain Science, Brown University, Providence, Rhode Island, 02912, USA
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Kim TK, Hong J, Ryu D, Kim S, Byeon SY, Huh W, Kim K, Baek GH, Kim HS. Identifying and extracting bark key features of 42 tree species using convolutional neural networks and class activation mapping. Sci Rep 2022; 12:4772. [PMID: 35306532 PMCID: PMC8934343 DOI: 10.1038/s41598-022-08571-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 02/28/2022] [Indexed: 11/19/2022] Open
Abstract
The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs.
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Katal N, Rzanny M, Mäder P, Wäldchen J. Deep Learning in Plant Phenological Research: A Systematic Literature Review. FRONTIERS IN PLANT SCIENCE 2022; 13:805738. [PMID: 35371160 PMCID: PMC8969581 DOI: 10.3389/fpls.2022.805738] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016-2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field.
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Affiliation(s)
- Negin Katal
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | | | - Patrick Mäder
- Data-Intensive Systems and Visualisation, Technische Universität Ilmenau, Ilmenau, Germany
- Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany
| | - Jana Wäldchen
- Max Planck Institute for Biogeochemistry, Jena, Germany
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11
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Takaya K, Sasaki Y, Ise T. Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science. BREEDING SCIENCE 2022; 72:96-106. [PMID: 36045894 PMCID: PMC8987844 DOI: 10.1270/jsbbs.21062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/20/2021] [Indexed: 06/15/2023]
Abstract
Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost.
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Affiliation(s)
- Kosuke Takaya
- Graduate School of Agriculture, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
| | - Yu Sasaki
- Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
| | - Takeshi Ise
- Field Science Education and Research Center, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
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Abstract
Tourism is a phenomenon that dates back to ancient times. Ancient Greek philosophers recognised, adopted, and promoted the concept of rest-based tourism. Ecotourism is a particular type of tourism that connects with activities that take place in nature, without harming it, along with the herbal and animal wealth. According to estimates, the global ecotourism industry is currently booming due to various reasons, and it is becoming an important factor of sustainable regional development. This article presents the vision, work, and outcomes of project AdVENt, a project focusing natively in sustainable ecotourism through natural science and technological innovation. AdVENt’s study area includes the National Parks of Oiti (or Oeta) and Parnassus in Central Greece, where there is a remarkable native flora with a high endemism rate integrated with areas of cultural value and national and European hiking routes and paths of varying difficulty.
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13
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GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment. SUSTAINABILITY 2021. [DOI: 10.3390/su132111865] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Plant identification from images has become a rapidly developing research field in computer vision and is particularly challenging due to the morphological complexity of plants. The availability of large databases of plant images, and the research advancements in image processing, pattern recognition and machine learning, have resulted in a number of remarkably accurate and reliable image-based plant identification techniques, overcoming the time and expertise required for conventional plant identification, which is feasible only for expert botanists. In this paper, we introduce the GReek vAScular Plants (GRASP) dataset, a set of images composed of 125 classes of different species, for the automatic identification of vascular plants of Greece. In this context, we describe the methodology of data acquisition and dataset organization, along with the statistical features of the dataset. Furthermore, we present results of the application of popular deep learning architectures to the classification of the images in the dataset. Using transfer learning, we report 91% top-1 and 98% top-5 accuracy.
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Pärtel J, Pärtel M, Wäldchen J. Plant image identification application demonstrates high accuracy in Northern Europe. AOB PLANTS 2021; 13:plab050. [PMID: 34457230 PMCID: PMC8387968 DOI: 10.1093/aobpla/plab050] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/26/2021] [Indexed: 05/23/2023]
Abstract
Automated image-based plant identification has experienced rapid development and has been already used in research and nature management. However, there is a need for extensive studies on how accurately automatic plant identification works and which characteristics of observations and study species influence the results. We investigated the accuracy of the Flora Incognita application, a research-based tool for automated plant image identification. Our study was conducted in Estonia, Northern Europe. Photos originated from the Estonian national curated biodiversity observations database, originally without the intention to use them for automated identification (1496 photos, 542 species) were examined. Flora Incognita was also directly tested in field conditions in various habitats, taking images of plant organs as guided by the application (998 observations, 1703 photos, 280 species). Identification accuracy was compared among species characteristics: plant family, growth forms and life forms, habitat type and regional frequency. We also analysed image characteristics (plant organs, background, number of species in focus), and the number of training images that were available for particular species to develop the automated identification algorithm. From database images 79.6 % of species were correctly identified by Flora Incognita; in the field conditions species identification accuracy reached 85.3 %. Overall, the correct genus was found for 89 % and the correct plant family for 95 % of the species. Accuracy varied among different plant families, life forms and growth forms. Rare and common species and species from different habitats were identified with equal accuracy. Images with reproductive organs or with only the target species in focus were identified with greater success. The number of training images per species was positively correlated with the identification success. Even though a high accuracy has been already achieved for Flora Incognita, allowing its usage for research and practices, our results can guide further improvements of this application and automated plant identification in general.
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Affiliation(s)
- Jaak Pärtel
- Hugo Treffner Gymnasium, Munga 12, Tartu 51007, Estonia
| | - Meelis Pärtel
- Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, Tartu 51005, Estonia
| | - Jana Wäldchen
- Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
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Durso AM, Ruiz de Castañeda R, Montalcini C, Mondardini MR, Fernandez-Marques JL, Grey F, Müller MM, Uetz P, Marshall BM, Gray RJ, Smith CE, Becker D, Pingleton M, Louies J, Abegg AD, Akuboy J, Alcoba G, Daltry JC, Entiauspe-Neto OM, Freed P, de Freitas MA, Glaudas X, Huang S, Huang T, Kalki Y, Kojima Y, Laudisoit A, Limbu KP, Martínez-Fonseca JG, Mebert K, Rödel MO, Ruane S, Ruedi M, Schmitz A, Tatum SA, Tillack F, Visvanathan A, Wüster W, Bolon I. Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite. Toxicon X 2021; 9-10:100071. [PMID: 34278294 PMCID: PMC8264216 DOI: 10.1016/j.toxcx.2021.100071] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 12/03/2022] Open
Abstract
The secretive behavior and life history of snakes makes studying their biology, distribution, and the epidemiology of venomous snakebite challenging. One of the most useful, most versatile, and easiest to collect types of biological data are photographs, particularly those that are connected with geographic location and date-time metadata. Photos verify occurrence records, provide data on phenotypes and ecology, and are often used to illustrate new species descriptions, field guides and identification keys, as well as in training humans and computer vision algorithms to identify snakes. We scoured eleven online and two offline sources of snake photos in an attempt to collect as many photos of as many snake species as possible, and attempt to explain some of the inter-species variation in photograph quantity among global regions and taxonomic groups, and with regard to medical importance, human population density, and range size. We collected a total of 725,565 photos-between 1 and 48,696 photos of 3098 of the world's 3879 snake species (79.9%), leaving 781 "most wanted" species with no photos (20.1% of all currently-described species as of the December 2020 release of The Reptile Database). We provide a list of most wanted species sortable by family, continent, authority, and medical importance, and encourage snake photographers worldwide to submit photos and associated metadata, particularly of "missing" species, to the most permanent and useful online archives: The Reptile Database, iNaturalist, and HerpMapper.
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Affiliation(s)
- Andrew M. Durso
- Department of Biological Sciences, Florida Gulf Coast University, Ft. Myers, FL, USA
- Institute of Global Health, Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Rafael Ruiz de Castañeda
- Institute of Global Health, Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- World Health Organization, Geneva, Switzerland
| | | | - M. Rosa Mondardini
- Citizen Science Center Zürich (ETH Zürich and University of Zürich), Zürich, Switzerland
| | | | | | | | - Peter Uetz
- The Reptile Database, Richmond, VA, USA
- Virginia Commonwealth University, Richmond, VA, USA
| | | | | | | | | | | | | | - Arthur D. Abegg
- Instituto Butantan, São Paulo, São Paulo, Brazil
- University of São Paulo, São Paulo, São Paulo, Brazil
| | - Jeannot Akuboy
- University of Kisangani, Kisangani, Democratic Republic of the Congo
| | | | - Jennifer C. Daltry
- Flora & Fauna International, Cambridge, England, UK
- Global Wildlife Conservation, Austin, TX, USA
| | | | - Paul Freed
- The Reptile Database, Richmond, VA, USA
- Reptile Database, Scotts Mills, OR, USA
| | | | - Xavier Glaudas
- University of the Witwatersrand, Johannesburg, South Africa
- Bangor University, Bangor, Wales, UK
| | - Song Huang
- Anhui Normal University, Wuhu, Anhui, China
| | | | - Yatin Kalki
- Madras Crocodile Bank Trust, Mahabalipuram, Tamil Nadu, India
| | | | | | | | | | - Konrad Mebert
- Global Biology, Birr, Switzerland
- Institute of Development, Ecology, Conservation & Cooperation, Rome, Italy
| | - Mark-Oliver Rödel
- Museum für Naturkunde - Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | | | - Manuel Ruedi
- Museum d'Histoire naturelle Geneve, Geneva, Switzerland
| | | | | | - Frank Tillack
- Museum für Naturkunde - Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | | | - Wolfgang Wüster
- Molecular Ecology and Fisheries Genetics Laboratory, School of Natural Sciences, Bangor University, Bangor, Wales, UK
| | - Isabelle Bolon
- Institute of Global Health, Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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16
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Mäder P, Boho D, Rzanny M, Seeland M, Wittich HC, Deggelmann A, Wäldchen J, Goslee S. The Flora Incognita app – Interactive plant species identification. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13611] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Patrick Mäder
- Computer and Systems Engineering Institute Technische Universität Ilmenau Ilmenau Germany
| | - David Boho
- Computer and Systems Engineering Institute Technische Universität Ilmenau Ilmenau Germany
| | | | - Marco Seeland
- Computer and Systems Engineering Institute Technische Universität Ilmenau Ilmenau Germany
| | - Hans Christian Wittich
- Computer and Systems Engineering Institute Technische Universität Ilmenau Ilmenau Germany
| | | | - Jana Wäldchen
- Max Planck Institute for Biogeochemistry Jena Germany
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Durso AM, Moorthy GK, Mohanty SP, Bolon I, Salathé M, Ruiz de Castañeda R. Supervised Learning Computer Vision Benchmark for Snake Species Identification From Photographs: Implications for Herpetology and Global Health. Front Artif Intell 2021; 4:582110. [PMID: 33959704 PMCID: PMC8093445 DOI: 10.3389/frai.2021.582110] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/09/2021] [Indexed: 11/29/2022] Open
Abstract
We trained a computer vision algorithm to identify 45 species of snakes from photos and compared its performance to that of humans. Both human and algorithm performance is substantially better than randomly guessing (null probability of guessing correctly given 45 classes = 2.2%). Some species (e.g., Boa constrictor) are routinely identified with ease by both algorithm and humans, whereas other groups of species (e.g., uniform green snakes, blotched brown snakes) are routinely confused. A species complex with largely molecular species delimitation (North American ratsnakes) was the most challenging for computer vision. Humans had an edge at identifying images of poor quality or with visual artifacts. With future improvement, computer vision could play a larger role in snakebite epidemiology, particularly when combined with information about geographic location and input from human experts.
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Affiliation(s)
- Andrew M Durso
- Department of Biological Sciences, Florida Gulf Coast University, Ft. Myers, FL, United States.,Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | | | | | - Isabelle Bolon
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marcel Salathé
- AICrowd, Lausanne, Switzerland.,Digital Epidemiology Laboratory, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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18
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Daley SK, Cordell GA. Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix. Nat Prod Commun 2021. [DOI: 10.1177/1934578x211003029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.
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Affiliation(s)
| | - Geoffrey A. Cordell
- Natural Products Inc., Evanston, IL, USA
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA
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19
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Durso AM, Bolon I, Kleinhesselink AR, Mondardini MR, Fernandez-Marquez JL, Gutsche-Jones F, Gwilliams C, Tanner M, Smith CE, Wüster W, Grey F, Ruiz de Castañeda R. Crowdsourcing snake identification with online communities of professional herpetologists and avocational snake enthusiasts. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201273. [PMID: 33614073 PMCID: PMC7890515 DOI: 10.1098/rsos.201273] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 12/11/2020] [Indexed: 06/12/2023]
Abstract
Species identification can be challenging for biologists, healthcare practitioners and members of the general public. Snakes are no exception, and the potential medical consequences of venomous snake misidentification can be significant. Here, we collected data on identification of 100 snake species by building a week-long online citizen science challenge which attracted more than 1000 participants from around the world. We show that a large community including both professional herpetologists and skilled avocational snake enthusiasts with the potential to quickly (less than 2 min) and accurately (69-90%; see text) identify snakes is active online around the clock, but that only a small fraction of community members are proficient at identifying snakes to the species level, even when provided with the snake's geographical origin. Nevertheless, participants showed great enthusiasm and engagement, and our study provides evidence that innovative citizen science/crowdsourcing approaches can play significant roles in training and building capacity. Although identification by an expert familiar with the local snake fauna will always be the gold standard, we suggest that healthcare workers, clinicians, epidemiologists and other parties interested in snakebite could become more connected to these communities, and that professional herpetologists and skilled avocational snake enthusiasts could organize ways to help connect medical professionals to crowdsourcing platforms. Involving skilled avocational snake enthusiasts in decision making could build the capacity of healthcare workers to identify snakes more quickly, specifically and accurately, and ultimately improve snakebite treatment data and outcomes.
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Affiliation(s)
- A. M. Durso
- Institute of Global Health, Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Department of Biological Sciences, Florida Gulf Coast University, Ft. Myers, FL, USA
| | - I. Bolon
- Institute of Global Health, Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - A. R. Kleinhesselink
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - M. R. Mondardini
- Citizen Science Center Zürich, ETHZ and University of Zürich, Zurich, Switzerland
| | | | - F. Gutsche-Jones
- Citizen Science Center Zürich, ETHZ and University of Zürich, Zurich, Switzerland
| | - C. Gwilliams
- Citizen Science Center Zürich, ETHZ and University of Zürich, Zurich, Switzerland
| | - M. Tanner
- Citizen Science Center Zürich, ETHZ and University of Zürich, Zurich, Switzerland
| | | | - W. Wüster
- Bangor University College of Natural Sciences, Bangor, UK
| | - F. Grey
- Citizen Cyberlab, University of Geneva, Geneva, Switzerland
| | - R. Ruiz de Castañeda
- Institute of Global Health, Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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20
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Rzanny M, Wittich HC, Mäder P, Deggelmann A, Boho D, Wäldchen J. Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant Perspectives. FRONTIERS IN PLANT SCIENCE 2021; 12:804140. [PMID: 35154194 PMCID: PMC8826579 DOI: 10.3389/fpls.2021.804140] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/20/2021] [Indexed: 05/07/2023]
Abstract
Poaceae represent one of the largest plant families in the world. Many species are of great economic importance as food and forage plants while others represent important weeds in agriculture. Although a large number of studies currently address the question of how plants can be best recognized on images, there is a lack of studies evaluating specific approaches for uniform species groups considered difficult to identify because they lack obvious visual characteristics. Poaceae represent an example of such a species group, especially when they are non-flowering. Here we present the results from an experiment to automatically identify Poaceae species based on images depicting six well-defined perspectives. One perspective shows the inflorescence while the others show vegetative parts of the plant such as the collar region with the ligule, adaxial and abaxial side of the leaf and culm nodes. For each species we collected 80 observations, each representing a series of six images taken with a smartphone camera. We extract feature representations from the images using five different convolutional neural networks (CNN) trained on objects from different domains and classify them using four state-of-the art classification algorithms. We combine these perspectives via score level fusion. In order to evaluate the potential of identifying non-flowering Poaceae we separately compared perspective combinations either comprising inflorescences or not. We find that for a fusion of all six perspectives, using the best combination of feature extraction CNN and classifier, an accuracy of 96.1% can be achieved. Without the inflorescence, the overall accuracy is still as high as 90.3%. In all but one case the perspective conveying the most information about the species (excluding inflorescence) is the ligule in frontal view. Our results show that even species considered very difficult to identify can achieve high accuracies in automatic identification as long as images depicting suitable perspectives are available. We suggest that our approach could be transferred to other difficult-to-distinguish species groups in order to identify the most relevant perspectives.
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Affiliation(s)
- Michael Rzanny
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
- *Correspondence: Michael Rzanny
| | - Hans Christian Wittich
- Data-intensive Systems and Visualisation, Technische Universität Ilmenau, Ilmenau, Germany
| | - Patrick Mäder
- Data-intensive Systems and Visualisation, Technische Universität Ilmenau, Ilmenau, Germany
- Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany
| | - Alice Deggelmann
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - David Boho
- Data-intensive Systems and Visualisation, Technische Universität Ilmenau, Ilmenau, Germany
| | - Jana Wäldchen
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
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21
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Boho D, Rzanny M, Wäldchen J, Nitsche F, Deggelmann A, Wittich HC, Seeland M, Mäder P. Flora Capture: a citizen science application for collecting structured plant observations. BMC Bioinformatics 2020; 21:576. [PMID: 33317442 PMCID: PMC7734824 DOI: 10.1186/s12859-020-03920-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 12/02/2020] [Indexed: 12/30/2022] Open
Abstract
Background Digital plant images are becoming increasingly important. First, given a large number of images deep learning algorithms can be trained to automatically identify plants. Second, structured image-based observations provide information about plant morphological characteristics. Finally in the course of digitalization, digital plant collections receive more and more interest in schools and universities. Results We developed a freely available mobile application called Flora Capture allowing users to collect series of plant images from predefined perspectives. These images, together with accompanying metadata, are transferred to a central project server where each observation is reviewed and validated by a team of botanical experts. Currently, more than 4800 plant species, naturally occurring in the Central European region, are covered by the application. More than 200,000 images, depicting more than 1700 plant species, have been collected by thousands of users since the initial app release in 2016. Conclusion Flora Capture allows experts, laymen and citizen scientists to collect a digital herbarium and share structured multi-modal observations of plants. Collected images contribute, e.g., to the training of plant identification algorithms, but also suit educational purposes. Additionally, presence records collected with each observation allow contribute to verifiable records of plant occurrences across the world.
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Affiliation(s)
- David Boho
- Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, 98693, Ilmenau, Germany
| | - Michael Rzanny
- Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
| | - Jana Wäldchen
- Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany.
| | - Fabian Nitsche
- Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, 98693, Ilmenau, Germany
| | - Alice Deggelmann
- Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745, Jena, Germany
| | - Hans Christian Wittich
- Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, 98693, Ilmenau, Germany
| | - Marco Seeland
- Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, 98693, Ilmenau, Germany
| | - Patrick Mäder
- Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, 98693, Ilmenau, Germany
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22
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Jones HG. What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora. AOB PLANTS 2020; 12:plaa052. [PMID: 33173573 PMCID: PMC7640754 DOI: 10.1093/aobpla/plaa052] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 09/22/2020] [Indexed: 05/23/2023]
Abstract
There has been a recent explosion in development of image recognition technology and its application to automated plant identification, so it is timely to consider its potential for field botany. Nine free apps or websites for automated plant identification and suitable for use on mobile phones or tablet computers in the field were tested on a disparate set of 38 images of plants or parts of plants chosen from the higher plant flora of Britain and Ireland. There were large differences in performance with the best apps identifying >50 % of samples tested to genus or better. Although the accuracy is good for some of the top-rated apps, for any quantitative biodiversity study or for ecological surveys, there remains a need for validation by experts or against conventional floras. Nevertheless, the better-performing apps should be of great value to beginners and amateurs and may usefully stimulate interest in plant identification and nature. Potential uses of automated image recognition plant identification apps are discussed and recommendations made for their future use.
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Affiliation(s)
- Hamlyn G Jones
- Division of Plant Sciences, School of Life Sciences, University of Dundee at the James Hutton Institute, Invergowrie, Dundee, UK
- School of Agriculture and Environment, University of Western Australia, Perth, WA, Australia
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23
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Zhao YF, Gao X, Hu JF, Chen Z, Chen Z. Tree species identification based on the fusion of bark and leaves. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:4018-4033. [PMID: 32987565 DOI: 10.3934/mbe.2020222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
For trees, leaves are often used for identification, but the shape of leaves changes greatly, bark will be another identifying feature. However, it is difficult to recognize by a single organ when there are intra class differences and inter class similarities between leaves or bark. So we fuse features of leaf and bark. Firstly, we collected 17 species of leaves and bark of trees through field shooting and web crawling. Then propose a method of combining convolution neural network (CNN) with cascade fusion, additive fusion algorithm, bilinear fusion and score level fusion. Finally, the features extracted from the leaves and bark are fused in the ReLu layer and Fully connected layer. The method was compared with single organ recognition, Support Vector Machines (SVM), and existing fusion methods, results show that the two organ fusion method proposed are better than the other recognition methods, and recognition accuracy is 87.86%. For similar trees, when it is impossible to accurately determine its species by a single organ, the fusion of two organs can effectively improve this situation.
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Affiliation(s)
- Ya Feng Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 154000, China
| | - Xuan Gao
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 154000, China
| | - Jun Feng Hu
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 154000, China
| | - Zhe Chen
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 154000, China
| | - Zhen Chen
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 154000, China
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24
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Kattenborn T, Eichel J, Fassnacht FE. Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Sci Rep 2019; 9:17656. [PMID: 31776370 PMCID: PMC6881311 DOI: 10.1038/s41598-019-53797-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 11/06/2019] [Indexed: 11/08/2022] Open
Abstract
Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.
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Affiliation(s)
- Teja Kattenborn
- Institute of Geography and Geoecology (IFGG), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany.
| | - Jana Eichel
- Institute of Geography and Geoecology (IFGG), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany
| | - Fabian Ewald Fassnacht
- Institute of Geography and Geoecology (IFGG), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany
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