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Computational Botany

Methods for Automated Species Identification

  • Book
  • © 2017

Overview

  • Describes cutting-edge methods for feature extraction, analysis and classification of leaves
  • Discusses machine learning techniques for plant leaf analysis
  • Analyzes the performance of plant scientists carrying out simply leaf recognition tasks
  • Gives new insights into the development of automatic plant identification systems
  • Includes supplementary material: sn.pub/extras

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About this book

This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist’s perspective. It then concludes with a chapter on the characterization of botanists' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert’s fixation process. The book not only represents an authoritative guide to advanced computational tools for plant identification, but provides experts in botany, computer science and pattern recognition with new ideas and challenges. As such it is expected to foster both closer collaborations and further technological developments in the emerging field of automatic plant identification.

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Table of contents (5 chapters)

Reviews

“This book is a laudable outcome of an interdisciplinary research collaboration. … it is an important source in the development of highly automated approaches of plant identification. … an important contribution for students and researchers in plant science as well as computational botanists.” (S. Suresh Ramanan and Anatoliy A. Khapugin, Current Science, Vol. 115 (2), July, 2018)



“A multidisciplinary domain that indirectly supports information technology (IT) and botanic areas--image analysis modeling and the associated algorithms for implementing automatic systems for plant species identification--is addressed in this book. … The book is very well written, in a clear and well-structured style, and is an excellent recommendation for specialists, experts and students in the field of computational botany.” (Computing Reviews, September, 2017) 

Authors and Affiliations

  • Faculty of Computing, Information Systems and Mathematics, Kingston University, Surrey, United Kingdom

    Paolo Remagnino

  • Department of Identification and Naming, Royal Botanic Gardens, Kew, Richmond, Surrey, United Kingdom

    Simon Mayo

  • Department of Natural Capital and Plant Health, Royal Botanic Gardens, Kew, Richmond, Surrey, United Kingdom

    Paul Wilkin

  • Digital Imaging Research Centre, Kingston University, Surrey, United Kingdom

    James Cope

  • Department of Biodiversity Informatics and Spatial Analysis, Royal Botanic Gardens, Kew, Richmond, Surrey, United Kingdom

    Don Kirkup

Bibliographic Information

  • Book Title: Computational Botany

  • Book Subtitle: Methods for Automated Species Identification

  • Authors: Paolo Remagnino, Simon Mayo, Paul Wilkin, James Cope, Don Kirkup

  • DOI: https://doi.org/10.1007/978-3-662-53745-9

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag GmbH Germany 2017

  • Hardcover ISBN: 978-3-662-53743-5Published: 16 December 2016

  • Softcover ISBN: 978-3-662-57156-9Published: 05 July 2018

  • eBook ISBN: 978-3-662-53745-9Published: 09 December 2016

  • Edition Number: 1

  • Number of Pages: VIII, 114

  • Number of Illustrations: 18 b/w illustrations, 20 illustrations in colour

  • Topics: Computational Intelligence, Plant Sciences, Computer Imaging, Vision, Pattern Recognition and Graphics

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