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001 9781315177304
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006 m o d
007 cr cnu---unuuu
008 200915t20212021flua ob 001 0 eng d
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9781351709996
_qelectronic book
020 _a1351709992
_qelectronic book
020 _a9781351709989
_qelectronic publication
020 _a1351709984
_qelectronic publication
020 _a9781351709972
_qMobipocket electronic book
020 _a1351709976
_qMobipocket electronic book
020 _a9781315177304
_qelectronic book
020 _a1315177307
_qelectronic book
020 _z9781138038554
020 _z1138038555
020 _z9780367565121
024 7 _a10.1201/9781315177304
_2doi
035 _a(OCoLC)1195455307
_z(OCoLC)1195437191
_z(OCoLC)1203956294
_z(OCoLC)1226072475
035 _a(OCoLC-P)1195455307
050 4 _aTA1637
_b.I475 2021
072 7 _aCOM
_x012000
_2bisacsh
072 7 _aSCI
_x011000
_2bisacsh
072 7 _aSCI
_x086000
_2bisacsh
072 7 _aTVB
_2bicssc
082 0 4 _a576.5/30285642
_223
245 0 0 _aIntelligent image analysis for plant phenotyping /
_cedited by Ashok Samal and Sruti Das Choudhury.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2021.
264 4 _c©2021
300 _a1 online resource (xix, 326 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aCover -- 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
505 8 _a1.8 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
505 8 _a2.1 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
505 8 _a2.3 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
505 8 _a3.6.5 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
520 _a"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"--
_cProvided by publisher.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_913839
650 0 _aPhenotype.
_913840
650 7 _aCOMPUTERS / Computer Graphics / General
_2bisacsh
_912490
650 7 _aSCIENCE / Life Sciences / Botany
_2bisacsh
_910964
650 7 _aSCIENCE / Life Sciences / General
_2bisacsh
_913841
700 1 _aSamal, Ashok,
_eeditor.
_913842
700 1 _aChoudhury, Sruti Das,
_eeditor.
_913843
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781315177304
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cEBK
999 _c70536
_d70536