Intelligent Image Analysis for Plant Phenotyping (eBook, PDF)

Redaktion: Samal, Ashok; Das Choudhury, Sruti

• Format: PDF

Produktbeschreibung

Domesticated crops are the result of artificial selection for particular phenotypes or, in some cases, natural selection for an adaptive trait. Plant traits can be identified through image-based plant phenotyping, a process that was, until recently, strenous and time-consuming. Intelligent Image Analysis for Plant Phenotyping reviews information on time-saving techniques, using computer vision and imaging technologies. These methodologies provide an automated, non-invasive, and scalable mechanism by which to define and collect plant phenotypes. Beautifully illustrated, with numerous color images, the book focuses on phenotypes measured from individual plants under controlled experimental conditions, which are widely available in high-throughput systems.

Features:

• Presents methodologies for image processing, including data-driven and machine learning techniques for plant phenotyping.
• Features information on advanced techniques for extracting phenotypes through images and image sequences captured in a variety of modalities.
• Includes real-world scientific problems, including predicting yield by modeling interactions between plant data and environmental information.
• Discusses the challenge of translating images into biologically informative quantitative phenotypes.

A practical resource for students, researchers, and practitioners, this book is invaluable for those working in the emerging fields at the intersection of computer vision and plant sciences.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 346
• Erscheinungstermin: 21. Oktober 2020
• Englisch
• ISBN-13: 9781351709996
• Artikelnr.: 60121815

Autorenporträt

Ashok Samal is a Professor in the Department of Computer Science and Engineering at the University of Nebraska-Lincoln, USA. He received Bachelor of Technology from the Indian Institute of Technology, Kanpur, India, and Ph.D. from the University of Utah, Salt Lake City, USA. His research interests include computer vision and data mining, and he has published extensively in these areas. More recently, he has focused on plant phenotyping and co-leads the Plant Vision Initiative research group at the University of Nebraska-Lincoln. Sruti Das Choudhury is a Research Assistant Professor in the School of Natural Resources at the University of Nebraska-Lincoln, USA. Previously, she was a Postdoctoral Research Associate in the Department of Computer Science and Engineering at the University of Nebraska-Lincoln and an Early Career Research Fellow in the Institute of Advanced Study at the University of Warwick, UK. She received Bachelor of Technology in Information Technology from the West Bengal University of Technology and Master of Technology in Computer Science and Application from the University of Calcutta, India. She obtained her Ph.D. in Computer Science Engineering from the University of Warwick, UK. Her research focus is on biometrics, data science, and most recently, image-based plant phenotyping analysis. She co-leads the Plant Vision Initiative research group at the University of Nebraska-Lincoln.

Inhaltsangabe

PART I Basics Chapter 1 Image-Based Plant Phenotyping: Opportunities and
Challenges Chapter 2 Multisensor Phenotyping for Crop Physiology Chapter 3
Image Processing Techniques for Plant Phenotyping PART II Techniques
Chapter 4 Segmentation Techniques and Challenges in Plant Phenotyping
Chapter 5 Structural High-Throughput Plant Phenotyping Based on Image
Sequence Analysis Chapter 6 Geometry Reconstruction of Plants Chapter 7
Image-Based Structural Phenotyping of Stems and Branches Chapter 8 Time
Series- and Eigenvalue-Based Analysis of Plant Phenotypes Chapter 9
Data-Driven Techniques for Plant Phenotyping Using Hyperspectral Imagery
Chapter 10 Machine Learning and Statistical Approaches for Plant
Phenotyping Chapter 11 A Brief Introduction to Machine Learning and Deep
Learning for Computer Vision PART III Practice Chapter 12 Chlorophyll a
Fluorescence Analyses to Investigate the Impacts of Genotype, Species, and
Stress on Photosynthetic Efficiency and Plant Productivity Chapter 13
Predicting Yield by Modeling Interactions between Canopy Coverage Image
Data, Genotypic and Environmental Information for Soybeans Chapter 14 Field
Phenotyping for Salt Tolerance and Imaging Techniques for Crop Stress
Biology Chapter 15 The Adoption of Automated Phenotyping by Plant Breeders