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Intelligent image analysis for plant phenotyping / edited by Ashok Samal and Sruti Das Choudhury.

Contributor(s): Samal, Ashok [editor.] | Choudhury, Sruti Das [editor.]Material type: TextTextPublisher: Boca Raton, FL : CRC Press, 2021Copyright date: ©2021Edition: First editionDescription: 1 online resource (xix, 326 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781351709996; 1351709992; 9781351709989; 1351709984; 9781351709972; 1351709976; 9781315177304; 1315177307Subject(s): Image processing -- Digital techniques | Computer vision | Phenotype | COMPUTERS / Computer Graphics / General | SCIENCE / Life Sciences / Botany | SCIENCE / Life Sciences / GeneralDDC classification: 576.5/30285642 LOC classification: TA1637 | .I475 2021Online resources: Taylor & Francis | OCLC metadata license agreement
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
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
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
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
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.
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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

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

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

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

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

"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.

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