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Full Description
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.
Contents
Table of contents
Edge-based machine learning for computer vision in smart plant biology imaging
Machine Learning for Studying Plant Evolutionary Developmental Biology
Machine Learning for Plant High-Throughput Phenotyping
Machine Learning for Studying Plant Secondary Metabolites
Machine Learning for Plant Ecological Research
Machine Learning for Modelling Plant Abiotic Stress Responses
Machine Learning for Modelling Plant-Pathogen Interactions
Machine Learning-Enhanced Plant Disease Detection and Management
Machine Learning for Analysing and Integrating Multiple Omics
Machine Learning for Plant Single-Cell RNA Sequencing
Machine Learning for Plant Genomic Prediction
Machine Learning-Assisted Plant Systems Biology
Machine learning-driven precision plant breeding
Machine Learning-Driven Smart Agriculture
Plant Leaf Disease Detection and Classification Using Convolutional Neural Networks
The Future Farming: Machine Learning and Crop Health
Social Impact of Machine Learning on agricultural Communities
Ethical and regulatory considerations of machine learning in modern agriculture
