A comprehensive and current summary of machine learning-based strategies for constructing digital plant biology
Machine Learning for Plant Biology provides a comprehensive summary of the latest developments in machine learning (ML) technologies, emphasizing their role in analyzing complex biological networks of plants and in modeling the responses of major crops to biotic and abiotic stresses. The combinatorial strategies discussed in this book enable readers to further their understanding of plant biology, stress physiology, and protection.
Machine Learning for Plant Biology includes information on:
- Intelligent breeding for stress-resistant and high-yield crops, contributing to sustainable agriculture, the Sustainable Development Goals (SDGs), and the Paris Agreement
- Interactions between plants, pathogens, and environmental stresses through omics approaches, functional genomics, genome editing, and high-throughput technologies
- State-of-the-art AI tools, including machine and deep learning models, as well as generative AI
- Applications include species identification, systems biology, functional genomics, genomic selection, phenotyping, synthetic biology, spatial omics, plant disease diagnosis and protection, and plant secondary metabolism
Machine Learning for Plant Biology is an essential reference on the subject for scientists, plant biologists, crop breeders, and students interested in the development of sustainable agriculture in the face of a changing global climate.
