AI Technologies for Crop Breeding

Name: AI Technologies for Crop Breeding
Price: 150 USD
Availability: InStock
ISBN: 9780443336331

by Jen-Tsung Chen

1st Edition

Publisher: Academic Press

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Science Agricultural Sciences

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Book Details

Print ISBN	9780443336331
eText ISBN	9780443336348
Publisher	Academic Press
Publishing Year	2025
Edition	1st Edition
Language	English
Pages	318

AI Technologies for Crop Breeding, 1st Edition, is an academic reference volume detailing artificial intelligence models implemented to improve plant health and agricultural production. Designed specifically for researchers and advanced-level students in crop breeding and genetics, the book frames how computational algorithms process complex biological systems to support advanced plant science research.

The thematic focus moves systematically through core analytical domains in modern agriculture. Detailed sections highlight technological advancements across genomic selection and genome-wide association studies, demonstrating how specific models handle large genetic datasets. The coverage further expands into phenomics and plant transcriptomics, illustrating how quantitative analytical tools extract patterns from diverse experimental measurements.

A defining thematic feature includes a review of how AI-based technologies align with UN Sustainable Development Goals. By linking computational methodology directly to broader global sustainability objectives, the volume offers clear guidance for academic specialists seeking structured insights into modern agricultural genetics.

Chapter 1: Advances in artificial intelligence for plant biology and crop breeding: An overview
Chapter 2: Technical development and current applications of artificial intelligence and machine learning in plant functional genomics
Chapter 3: Next-generation smart crop breeding based on integrated artificial intelligence models and multiple omics: Methods and applications
Chapter 4: The role of artificial intelligence in organizing climate-resilient and smart agriculture
Chapter 5: Machine learning-assisted genome-wide association study (GWAS) in plants
Chapter 6: Integrated multiple omics and artificial intelligence for plant phenotyping and phenomics
Chapter 7: Deep generative models for studying and integrating plant multiple omics
Chapter 8: Deep learning, generative artificial intelligence and synthetic biology for crop breeding
Chapter 9: Exploration of plant single-cell genomics assisted by artificial intelligence technologies: Updated protocols and applications
Chapter 10: Artificial intelligence models for plant genomic selection
Chapter 11: Artificial intelligence for unrevealing plant stress regulating networks and responses
Chapter 12: Hub gene prediction by machine learning for regulating plant stress responses
Chapter 13: Machine learning for uncovering plant-pathogen interactions
Chapter 14: Machine learning for advancing plant high-throughput technologies
Chapter 15: Artificial intelligence models for meta-analyzing plant transcriptomic
Chapter 16: Integrating artificial intelligence technologies with plant systems biology
Chapter 17: Applications of artificial intelligence in plant genomics, genome editing and biotechnology
Chapter 18: Artificial intelligence, automation and the Internet of Things for smart agriculture: Updated methods and current applications
Chapter 19: Limitations and future perspective of artificial intelligence in crop breeding and agriculture