Intelligent Image Analysis for Plant Phenotyping
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.
- 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.
PART I Basics
Chapter 1 Image-Based Plant Phenotyping: Opportunities and Challenges
[Ashok Samal, Sruti Das Choudhury, and Tala Awada]
Chapter 2 Multisensor Phenotyping for Crop Physiology
[Stefan Paulus, Gustavo Bonaventure, and Marcus Jansen]
Chapter 3 Image Processing Techniques for Plant Phenotyping
[Bashyam Srinidhi and Sanjiv Bhatia]
PART II Techniques
Chapter 4 Segmentation Techniques and Challenges in Plant Phenotyping
[Sruti Das Choudhury]
Chapter 5 Structural High-Throughput Plant Phenotyping Based on Image
[Sruti Das Choudhury and Ashok Samal]
Chapter 6 Geometry Reconstruction of Plants
[Ayan Chaudhury and Christophe Godin]
Chapter 7 Image-Based Structural Phenotyping of Stems and Branches
[Fumio Okura, Takahiro Isokane, Ayaka Ide, Yasuyuki
Matsushita, and Yasushi Yagi]
Chapter 8 Time Series- and Eigenvalue-Based Analysis of Plant Phenotypes
[Sruti Das Choudhury, Saptarsi Goswami, and Amlan Chakrabarti]
Chapter 9 Data-Driven Techniques for Plant Phenotyping Using
[Suraj Gampa and Rubi Quiñones]
Chapter 10 Machine Learning and Statistical Approaches for Plant
[Zheng Xu and Cong Wu]
Chapter 11 A Brief Introduction to Machine Learning and Deep Learning
for Computer Vision
[Eleanor Quint and Stephen Scott]
PART III Practice
Chapter 12 Chlorophyll a Fluorescence Analyses to Investigate the Impacts
of Genotype, Species, and Stress on Photosynthetic Efficiency
and Plant Productivity
[Carmela Rosaria Guadagno and Brent E. Ewers]
Chapter 13 Predicting Yield by Modeling Interactions between Canopy
Coverage Image Data, Genotypic and Environmental
Information for Soybeans
[Diego Jarquin, Reka Howard, Alencar Xavier, and Sruti Das
Chapter 14 Field Phenotyping for Salt Tolerance and Imaging Techniques
for Crop Stress Biology
[Shayani Das Laha, Amlan Jyoti Naskar, Tanmay Sarkar,
Suman Guha, Hossain Ali Mondal, and Malay Das]
Chapter 15 The Adoption of Automated Phenotyping by Plant Breeders
[Lana Awada, Peter W. B. Phillips, and Stuart J. Smyth]