AI Technologies for Crop Breeding

Jen-Tsung Chen

• Publisher: Academic Press
• Year: 2025
• Topic: Technology & Engineering
• eBook ISBN:
  9780443336348
• Subtopic: Agribusiness
• Index: Technology & Engineering

AI Technologies for Crop Breeding offers the latest insights into the use of artificial intelligence models to improve plant health and production. Presenting applications of AI technologies in plant biology, biotechnology, and crop breeding, it explores practices for the mitigation of biotic and abiotic stressors as well as other plant growth challenges.AI-based technologies are expected to advance approaches to plant functional genomics and multiple omics, resulting in smarter and more efficient crop breeding for next-generation agriculture helping to address the challenges of the increasing human population and the globally changing climate. AI tools such as machine learning, particularly deep learning, have been applied to predict chief players in complicated biological networks, increasing the understanding of in-depth mechanisms of plant-pathogen and plant-environment interactions. Additionally, responses of plants facing stress can be modeled using AI technologies, and the resulting data are valuable not only to plant stress physiology but also for stress-resilient and disease-resistant crop breeding.This book introduces AI technologies for studying plant biology, focusing on machine learning and deep learning models for integrating multiple omics approaches and revealing the knowledge of plant functional genomes. Technological advancements and emerging applications of machine learning and deep learning in genomic selection, genome-wide association study (GWAS), phenotyping and constructing phenomics, and transcriptomics are also featured in this book.AI Technologies for Crop Breeding is an ideal reference for researchers, academics, and advanced-level students and professors in the fields of plant sciences, plant stress physiology, bioinformatics, systems biology, and crop breeding. - Reviews AI-based technologies in crop plant functional genomics - Presents integration of AI tools with high-throughput omics - Advances understanding of the potential impact of AI technologies in addressing the UN Sustainable Development Goals

Table of contents

• Title of Book
• 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. The role of artificial intelligence in developing climate-resilient and smart agriculture
• Chapter 4. Artificial intelligence models for plant genomic selection
• Chapter 5. Prediction of hub genes using machine learning for the regulation of plant stress responses
• Chapter 6. Machine learning for uncovering plant–pathogen interactions
• Chapter 7. Applications of artificial intelligence in plant genomics, genome editing, and biotechnology
• Chapter 8. Deep generative models for studying and integrating multiple plant omics
• Chapter 9. Exploration of plant single-cell genomics assisted by artificial intelligence technologies: Updated protocols and applications
• Chapter 10. Advancements in AI-driven CRISPR-based crop breeding: A path to sustainable agriculture
• Chapter 11. AI and computational biology for advancing water stress–tolerant crop breeding
• Chapter 12. AI and computational biology for advancing heavy metal stress-tolerant crop breeding
• Chapter 13. AI and computational biology for advancing bacterial disease-tolerant crop breeding
• Chapter 14. Advancing crop breeding for viral disease tolerance using computational biology and AI
• Chapter 15. AI and computational biology for advancing fungal disease–tolerant crop breeding
• Chapter 16. AI models and metagenomics in crop breeding: Advancing soil and plant microbiome analysis through machine learning and deep learning approaches
• Chapter 17. Applications of generative AI for optimizing research in the agricultural business sector
• Chapter 18. Limitations and future perspectives of artificial intelligence in crop breeding and agriculture
• Index