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Machine Learning for Plant Biology
About this book
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.
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Machine Learning for Plant Biology by Jen-Tsung Chen in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Botany. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- Preface
- List of Contributors
- Chapter 1: Edge-Based Machine Learning for Computer Vision in Smart Plant Biology Imaging
- Chapter 2: Machine Learning for Studying Plant Evolutionary Developmental Biology
- Chapter 3: Machine Learning for Plant High-Throughput Phenotyping
- Chapter 4: Machine Learning for Studying Plant Secondary Metabolites
- Chapter 5: Machine Learning for Plant Ecological Research
- Chapter 6: Machine Learning for Modeling Plant Abiotic Stress Responses
- Chapter 7: Machine Learning for Modeling Plant–Pathogen Interactions
- Chapter 8: Machine Learning-Enhanced Plant Disease Detection and Management
- Chapter 9: Machine Learning for Analyzing and Integrating Multiple Omics
- Chapter 10: Machine Learning for Plant Single-Cell RNA Sequencing
- Chapter 11: Machine Learning for Plant Genomic Prediction
- Chapter 12: Machine Learning-Assisted Plant Systems Biology
- Chapter 13: Machine Learning-Driven Precision Plant Breeding
- Chapter 14: Machine Learning-Driven Smart Agriculture
- Chapter 15: Plant Leaf Disease Detection and Classification Using Convolutional Neural Networks
- Chapter 16: The Future Farming: Machine Learning and Crop Health
- Chapter 17: Social Impact of Machine Learning on Agricultural Communities
- Chapter 18: Ethical and Regulatory Considerations of Machine Learning in Modern Agriculture
- Index
- End User License Agreement