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Full Description
Predicting plant gene functions using AI-enhanced genomic prediction models is becoming a key component in crop breeding. This technology will significantly advance the development of new crops resistant to a wide range of stresses. This book describes the application of AI-enhanced genomic prediction models and their integration with high-throughput technology, such as smart phenotyping, multiple omics data from metabolomics, proteomics, and transcriptomics, to accelerate crop improvement. Written by a team of experts in this field, this book describes new methods for the production of climate change-ready, disease-resistant, stress-resilient crops by advancing the next generation of plant breeding tools. It is an ideal reference for readers working in all aspects of plant sciences and agricultural biotechnology, particularly plant stress physiology and crop breeding. The book: Describes AI-empowered models for accelerating plant genomic selection Comprehensively reviews omics-assisted genomic prediction for crop breeding Includes ways to develop climate change-ready and disease-resistant future crops
Contents
1: An Introductory Overview of Plant Genomic Prediction 2: AI-Based Multi-Omics for Predicting Plant Complex Traits 3: AI-Based Multiple Omics Data Integration for Plant Genomic Prediction 4: AI-Based Genotype-to-Phenotype Prediction in Crop Breeding 5: Data-Driven Genomic Design for Next-Generation Crop Breeding 6: Plant Growth Models in Genomic Prediction 7: Genomic Prediction Models for Cereal Crops 8: Genomic Prediction Models for Fruit and Vegetable Crops 9: Genomic Prediction Models for Trees 10: High-Throughput and Precision Phenotyping for Plant Genomic Prediction 11: Validation Methods for Plant Genomic Prediction 12: Genomic Prediction for Developing Climate Change-Ready Crops 13: Genomic Prediction for Developing Salinity-Resilient and Drought-Tolerant Crops 14: AI-Driven Multidimensional Omics Approaches for Advancing Plant Defence 15: Enhancing Genomic Prediction through Kernel Methods 16: Artificial Intelligence Approaches In Plant Data Science 17: Deep Learning in Plant Pest Control 18: AI-Assisted Genomic and Epigenomic Modulations in Plant Stress Adaptation 19: Plant Genomic Prediction: Fundamental Models and Recent Applications in Crop Breeding
