edited by Jen-Tsung Chen
First edition
Hoboken, NJ : Wiley, 2026
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2025
computer file -- remote (wr)
pages cm
Content Type: text (rdacontent), Media Type: unmediated (rdamedia), Carrier Type: volume (rdacarrier)
Includes bibliographical references and index
Summary:"This book explores the integration of artificial intelligence (AI) and machine learning (ML) technologies in plant biology and agriculture. It highlights recent advancements in AI/ML-including deep learning and generative AI-and their applications across a wide range of subfields, such as species identification, functional genomics, phenotyping, stress physiology, plant disease management, genome editing, and smart agriculture. Focusing on the role of ML in analyzing complex biological networks, integrating multi-omics data, and enabling 3D biology through spatial and single-cell omics, the book presents cutting-edge tools and approaches to support sustainable agriculture in the face of climate change. It also discusses ethical considerations and regulatory aspects"-- Provided by publisher
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
United States
English (eng)
English (eng)
LCC:QK46.5.M3
9781394329649/9781394329632/9781394329625/9781394329618 (: e-book)
LCCN : 2025027934
