Description
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
1. Advances in artificial intelligence for plant biology and crop breeding: An overview2. Technical development and current applications of artificial intelligence and machine learning in plant functional genomics3. Next-generation smart crop breeding based on integrated artificial intelligence models and multiple omics: Methods and applications4. The role of artificial intelligence in organizing climate-resilient and smart agriculture5. Machine learning-assisted genome-wide association study (GWAS) in plants6. Integrated multiple omics and artificial intelligence for plant phenotyping and phenomics7. Deep generative models for studying and integrating plant multiple omics8. Deep learning, generative artificial intelligence and synthetic biology for crop breeding9. Exploration of plant single-cell genomics assisted by artificial intelligence technologies: Updated protocols and applications10. Artificial intelligence models for plant genomic selection11. Artificial intelligence for unrevealing plant stress regulating networks and responses12. Hub gene prediction by machine learning for regulating plant stress responses13. Machine learning for uncovering plant-pathogen interactions14. Machine learning for advancing plant high-throughput technologies15. Artificial intelligence models for meta-analyzing plant transcriptomic16. Integrating artificial intelligence technologies with plant systems biology17. Applications of artificial intelligence in plant genomics, genome editing and biotechnology18. Artificial intelligence, automation and the Internet of Things for smart agriculture: Updated methods and current applications19. Limitations and future perspective of artificial intelligence in crop breeding and agriculture
