Intelligent Image Analysis for Plant Phenotyping

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.

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