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.
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.
