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Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning [article]

Sohaib Younis, Marco Schmidt, Claus Weiland, Stefan Dressler, Bernhard Seeger, Thomas Hickler
2020 arXiv   pre-print
In our study we use deep learning to detect plant organs on digitized herbarium specimens with Faster R-CNN.  ...  For our experiment we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model  ...  In this paper we use deep learning for detecting plant organs on herbarium scans.  ... 
arXiv:2007.13106v2 fatcat:h3huklafxngwtdiwekwdxtjdyu

Detection and annotation of plant organs from digitised herbarium scans using deep learning

Sohaib Younis, Marco Schmidt, Claus Weiland, Stefan Dressler, Bernhard Seeger, Thomas Hickler
2020 Biodiversity Data Journal  
For our experiment, we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection  ...  In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN.  ...  Acknowledgements TH, SY, MS and SD received funding from the DFG Project "Mobilization of trait data from digital image files by deep learning approaches" (grant 316452578).  ... 
doi:10.3897/bdj.8.e57090 pmid:33343217 pmcid:PMC7746675 fatcat:zlyprcv2ujfspn4o4mdymf7hly

Machine Learning as a Service for DiSSCo's Digital Specimen Architecture

Jonas Grieb, Claus Weiland, Alex Hardisty, Wouter Addink, Sharif Islam, Sohaib Younis, Marco Schmidt
2021 Biodiversity Information Science and Standards  
and extraction of features like organs and morphological traits from digitized collection data (with a focus on herbarium sheets).  ...  Taking up the use case to detect and classify regions of interest (ROI) on herbarium scans, we demonstrate a MLaaS prototype for DiSSCo involving the digital object framework, Cordra, for the management  ...  Source code available at: https://github.com/jgrieb/plant-detection-service Keywords FAIR Digital Object, Distributed System of Scientific Collections, plant organ detection, deep learning, region-based  ... 
doi:10.3897/biss.5.75634 fatcat:2ixqugt7zfb7hmtfl7i5g2coci

Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review [article]

Burhan Rashid Hussein, Owais Ahmed Malik, Wee-Hong Ong, Johan Willem Frederik Slik
2021 arXiv   pre-print
In this study, a thorough analysis and comparison of more than 50 peer-reviewed studies which focus on application of computer vision and machine learning techniques to digitized herbarium specimen have  ...  This presents a perfect time to automate and speed up more novel discoveries using machine learning and computer vision.  ...  Acknowledgements Declaration of Interest All authors declared that there is no any form of financial or personal interest that may have influence this work.  ... 
arXiv:2104.08732v1 fatcat:kags7wxjlfh53igpzdlivox75e

A deep learning-based approach for segmenting and counting reproductive organs from digitized herbarium specimen images using refined Mask Scoring R-CNN

Abdelaziz Triki, Bassem Bouaziz, Jitendra Gaikwad, Walid Mahdi
2021 Tunisian-Algerian Joint Conference on Applied Computing  
The use of machine learning techniques, including deep learning, has recently been shown to be helpful in this endeavor.  ...  We proposed in this paper a deep learning method based on the refined Mask Scoring R-CNN approach to segment and count reproductive organs, including buds, flowers, and fruits from specimen images.  ...  Acknowledgments This work was part of the MAMUDS project (Management Multimedia Data for Science), supported by BMBF, Germany (Project No. 01D16009) and MHESR, Tunisia.  ... 
dblp:conf/tacc/TrikiBGM21 fatcat:nnbs5sagcrejrmp45qazocvx5i

Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest

Hervé Goëau, Titouan Lorieul, Patrick Heuret, Alexis Joly, Pierre Bonnet
2022 Plants  
We propose to investigate the extent to which the use of deep learning can help detect and type-classify these relatively rare vegetative structures in herbarium collections.  ...  Our results demonstrate the relevance of using herbarium data in vegetative phenology research as well as the potential of deep learning approaches for growing shoot detection.  ...  detection model based on deep learning, which requires a rather large number of training examples to learn from.  ... 
doi:10.3390/plants11040530 pmid:35214863 pmcid:PMC8875713 fatcat:usmvxdfnlveapa6ve66vei4qc4

Plants meet machines: Prospects in machine learning for plant biology

Pamela S. Soltis, Gil Nelson, Alina Zare, Emily K. Meineke
2020 Applications in Plant Sciences  
Zare, and E. K. Meineke. 2020. Plants meet machines: Prospects in machine learning for plant biology. Applications in Plant Sciences 8(6): e11371.  ...  The contributions of machine learning to the plant sciences, especially for automated species identification from images of digitized herbarium specimens, is showing great promise (Schuettpelz et al.,  ...  et al., 2016) object-detection application programming interface (API), an API designed to make supervised deep learning object detection accessible for plant scientists.  ... 
doi:10.1002/aps3.11371 fatcat:toamaekb7vboplxhjrlksy4ak4

Going deeper in the automated identification of Herbarium specimens

Jose Carranza-Rojas, Herve Goeau, Pierre Bonnet, Erick Mata-Montero, Alexis Joly
2017 BMC Evolutionary Biology  
Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants  ...  Conclusions: This is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria.  ...  Acknowledgements Thanks to the National Museum of Costa Rica for their help with the collection, identification, and digitization of samples in the Costa Rican leaf-scan dataset.  ... 
doi:10.1186/s12862-017-1014-z pmid:28797242 pmcid:PMC5553807 fatcat:xdqf5swekjgzrb4zjdd4v52mwe

Automated plant species identification—Trends and future directions

Jana Wäldchen, Michael Rzanny, Marco Seeland, Patrick Mäder, Alexander Bucksch
2018 PLoS Computational Biology  
Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-todate tools automating the process of species identification.  ...  discussion of open and future research thrusts.  ...  The Plant-CLEF2015/2016 dataset consists of images with different plant organs or plant views (i.e., entire plant, fruit, leaf, flower, stem, branch, and leaf scan).  ... 
doi:10.1371/journal.pcbi.1005993 pmid:29621236 pmcid:PMC5886388 fatcat:e2zgdgdg3bc67jtqmjr7bzcfny

A complete digitization of German herbaria is possible, sensible and should be started now

Thomas Borsch, Albert-Dieter Stevens, Eva Häffner, Anton Güntsch, Walter G. Berendsohn, Marc Appelhans, Christina Barilaro, Bánk Beszteri, Frank Blattner, Oliver Bossdorf, Helmut Dalitz, Stefan Dressler (+39 others)
2020 Research Ideas and Outcomes  
use of digital objects.  ...  Experiences from other countries like France, the Netherlands, Finland, the US and Australia show that herbaria can be comprehensively and cost-efficiently digitized in a relatively short time due to established  ...  Using artificial intelligence for biodiversity research and collection curation: Automated pathogen detection: Plant pests and pathogens can be detected in herbarium specimens (Böllmann and Scholler 2006  ... 
doi:10.3897/rio.6.e50675 fatcat:plaibdzxgjacljbsoqdkknnylu

Computer vision-based phenotyping for improvement of plant productivity: A machine learning perspective

Keiichi Mochida, Satoru Koda, Komaki Inoue, Takashi Hirayama, Shojiro Tanaka, Ryuei Nishii, Farid Melgani
2018 GigaScience  
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants.  ...  Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants  ...  Acknowledgements The authors gratefully thank Nobuko Kimura and Kyoko Ikebe for their assistance with the preparation of this manuscript.  ... 
doi:10.1093/gigascience/giy153 pmid:30520975 pmcid:PMC6312910 fatcat:zj6rmjqeczbr7cys6vdqxx6onm

Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species

Giulia Albani Rocchetti, Chelsey Geralda Armstrong, Thomas Abeli, Simone Orsenigo, Caroline Jasper, Simon Joly, Anne Bruneau, Maria Zytaruk, Jana C. Vamosi
2020 New Phytologist  
The increase of online digitization of natural history collections has now led to a surge of new studies on the uses of machine-learning.  ...  We find that they also offer material to foster species recovery, ecosystem restoration, and de-extinction, and these elements should be used in conjunction with machine learning and citizen science initiatives  ...  Tressou & Haevermans (2018) developed a method for plant species threat prediction (see Section IV, above) and Zizka et al. (2020) used deep learning for automated conservation assessment of at-risk  ... 
doi:10.1111/nph.17133 pmid:33280123 fatcat:teeivifbqjexxeetgaftrbbc5q

LifeCLEF 2015: Multimedia Life Species Identification Challenges [chapter]

Alexis Joly, Hervé Goëau, Hervé Glotin, Concetto Spampinato, Pierre Bonnet, Willem-Pier Vellinga, Robert Planqué, Andreas Rauber, Simone Palazzo, Bob Fisher, Henning Müller
2015 Lecture Notes in Computer Science  
deep learning.  ...  deep learning methods.  ... 
doi:10.1007/978-3-319-24027-5_46 fatcat:lq6ug6mrhbh3lpatr34mvuyxwy

Deep learning and computer vision will transform entomology [article]

Toke Thomas Hoye, Johanna Arje, Kim Bjerge, Oskar LP Hansen, Alexandros Iosifidis, Florian Leese, Hjalte Mann, Kristian Meissner, Claus Melvad, Jenni Raitoharju
2020 bioRxiv   pre-print
When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity.  ...  Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse.  ...  ongoing 64 embedded a digital camera and a microprocessor that can count trapped individuals in real-time 127 using object-detection based on an optimized deep learning model (37).  ... 
doi:10.1101/2020.07.03.187252 fatcat:wv3sn4jet5dr3llion5ua34ssu

Herbaria as Big Data Sources of Plant Traits

J. Mason Heberling
2021 International journal of plant sciences  
(including traits measured, approaches used, research questions answered); 2) What new trait-based insights can be made from herbarium specimens?  ...  Herbarium specimens have long been a cornerstone of taxonomic research but are only recently being recognized for their potential as a source of spatially and temporally extensive data on plant functional  ...  It will be published in its final form in an upcoming issue of International Journal of Plant Sciences, published by The University of Chicago Press.  ... 
doi:10.1086/717623 fatcat:dabejmg4wffw7demvrjcyf4mje
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