Meta-omics in Crop Improvement

Introduction

This second volume on meta-omics technologies brings together the rapidly growing facets of this discipline, focusing on its application in crop improvement.

By integrating metagenomics, metatranscriptomics, metaproteomics, and metabolomics, it aims to document progress and the integration of meta-omics technologies in crop research, showcasing case studies and applications while examining wider consequences of implementing meta-omics in agriculture.

Applications in Crop Research

This book features chapters discussing a range of applications of these methods across different crops, including rice, wheat, legumes, fibre crops, tobacco, soybean, horticultural crops, microalgae, and cyanobacteria.

Machine Learning in Meta-omics

A chapter focused on the integration of machine learning in metagenomics highlights the possibilities and obstacles in predictive modeling, big data analysis, and functional annotation.

Scientific and Practical Significance

Collectively, these chapters demonstrate both the scientific diversity of these approaches' applications across various crops and settings, as well as the forward-thinking consequences for enhancing crop development.

Target Audience

Researchers in the field of agricultural science, as well as practitioners interested in sustainable crop production, will find this volume invaluable.

It offers a comprehensive understanding of how meta-omics can be harnessed to address pressing agricultural challenges, making it a must-read for anyone committed to advancing global food security and sustainable agriculture.