Overview

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

Full Product Details

Author:   Professor Jen-Tsung Chen (National University of Kaohsiang, Taiwan)
Publisher:   CABI Publishing
Imprint:   CABI Publishing
ISBN:  

9781836991304

ISBN 10:   1836991304
Pages:   368
Publication Date:   01 May 2026
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
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Table of 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

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Author Information

Dr. Jen-Tsung Chen is a professor of cell biology at the National University of Kaohsiung in Taiwan. He teaches cell biology, genomics, proteomics, plant physiology, and plant biotechnology. Dr. Chen's research interests include bioactive compounds, chromatography techniques, plant molecular biology, plant biotechnology, bioinformatics, and systems pharmacology. He is an active editor of academic books and international journals to advance the exploration of multidisciplinary knowledge involving plant physiology, plant biotechnology, nanotechnology, materials science, ethnopharmacology, and systems biology. He serves as an associate editor, editorial board member, and guest editor in reputed journals. Dr. Chen has published books in collaboration with international publishers and is currently handling book projects on diverse topics, including AI technologies, drug discovery, drug development, herbal medicine, medicinal biotechnology, nanotechnology, bioengineering, plant functional genomics, plant speed breeding, epigenetics, functional RNAs, and CRISPR-based genome editing. Dr. Chen is a productive author in academic publications and was recognized as one of the World's Top 2% Scientists between 2023 to 2025 by Elsevier and Stanford University. Dr. Chen received the Springer Nature Editor of Distinction Award in 2025.

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