| Description |
1 online resource (xix, 326 pages) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Editors -- Contributors -- Part I Basics -- Chapter 1 Image-Based Plant Phenotyping: Opportunities and Challenges -- 1.1 Introduction -- 1.2 Importance of Phenotyping Research -- 1.3 Plant Phenotyping Analysis Framework -- 1.4 Plant Phenotyping Networks -- 1.5 Opportunities and Challenges Associated with High-Throughput Image-Based Phenotyping -- 1.6 Image-Based Plant Phenotyping Analysis -- 1.7 Data Management for Plant Phenotyping |
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1.8 Computational Challenges in Image-Based Plant Phenotyping -- 1.8.1 Computational Resources -- 1.8.2 Algorithm Robustness -- 1.8.3 Inference from Incomplete Information -- 1.8.4 Large Phenotype Search Space -- 1.8.5 Analysis of Image Sequences -- 1.8.6 Lack of Benchmark Datasets -- 1.9 Looking into the Future -- 1.9.1 Imaging Platforms -- 1.9.2 Integrated Phenotypes -- 1.9.3 Learning-Based Approaches -- 1.9.4 Shape Modeling and Simulation for Phenotyping -- 1.9.5 Event-Based Phenotypes -- 1.10 Summary -- References -- Chapter 2 Multisensor Phenotyping for Crop Physiology |
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2.1 Crop Phenotyping -- 2.1.1 Breeding for Crop Performance and Yield -- 2.1.2 Purpose of Phenotypic Image Analysis -- 2.1.3 Intelligent Image Analysis -- 2.1.4 Critical Traits for Seed Crop Improvement -- 2.2 Cameras and Sensors for Crop Measurements -- 2.2.1 RGB Imaging -- 2.2.1.1 Greenhouse Parameters Recorded with RGB Cameras -- 2.2.1.2 Field Parameters -- 2.2.2 3D Laser Imaging -- 2.2.2.1 Greenhouse Parameters Recorded with Laser Scanners -- 2.2.2.2 Field Parameters Recorded with Laser Scanners -- 2.2.3 Fluorescence Imaging -- 2.2.3.1 Greenhouse Parameters -- 2.2.3.2 Field Parameters |
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2.3 Conclusions -- Acknowledgment -- References -- Chapter 3 Image Processing Techniques for Plant Phenotyping -- 3.1 Introduction -- 3.2 Goals of Plant Phenotyping -- 3.3 Background and Literature Survey -- 3.4 Image-Processing Methodology -- 3.5 Image Acquisition/Imaging Basics -- 3.5.1 Image Data Structures -- 3.5.2 Visible Light Images -- 3.5.3 Infrared Images -- 3.5.4 Hyperspectral and Multispectral Images -- 3.5.5 Fluorescent Images -- 3.6 Basic Image-Processing Operations -- 3.6.1 Grayscale Conversion -- 3.6.2 Histogram Processing -- 3.6.3 Thresholding -- 3.6.4 Edge Detection |
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3.6.5 Image Transformations -- 3.6.6 Segmentation -- 3.6.6.1 Frame Difference Segmentation -- 3.6.6.2 Color-Based Segmentation -- 3.6.7 Morphological Operations -- 3.6.7.1 Dilation and Erosion -- 3.6.7.2 Opening and Closing -- 3.6.8 Thinning -- 3.6.9 Connected Component Analysis -- 3.6.10 Skeletonization -- 3.6.10.1 Graphical Representation -- 3.7 Feature Computation -- 3.7.1 Basic Shape Properties -- 3.7.1.1 Length -- 3.7.1.2 Area -- 3.7.1.3 Bounding Box -- 3.7.1.4 Aspect Ratio -- 3.7.1.5 Convex Hull -- 3.7.1.6 Circularity -- 3.7.1.7 Straightness -- 3.7.2 Color Properties |
| Summary |
"Domesticated crops are the result of artificial selection for particular phenotypes and, in some case, natural selection for an adaptive trait. 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 to define and collect plant phenotypes. Beautifully illustrated with numerous color images, this book is invaluable for those working in the emerging fields at the intersection of computer vision and plant sciences"-- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
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 |
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Online resource; title from digital title page (viewed on November 19, 2020) |
| Subject |
Image processing -- Digital techniques.
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Computer vision.
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Phenotype.
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Phenotype
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digital imaging.
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COMPUTERS -- Computer Graphics -- General.
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SCIENCE -- Life Sciences -- Botany.
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SCIENCE -- Life Sciences -- General.
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Computer vision
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Image processing -- Digital techniques
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Phenotype
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| Form |
Electronic book
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| Author |
Samal, Ashok, editor.
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Choudhury, Sruti Das, editor.
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| ISBN |
9781351709996 |
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1351709992 |
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9781351709989 |
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1351709984 |
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9781351709972 |
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1351709976 |
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9781315177304 |
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1315177307 |
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