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