Advances in Agricultural and Industrial Microbiology Volume-2: Applications of Microbes for Sustainable Agriculture and in-silico Strategies

This book, the second volume of Advances in Agricultural and Industrial Microbiology is the compilation of modern technologies with scientific advancement in promoting plant growth by rhizobacterial biostimulants, endophytic microbes, and arbuscular mycorrhizal fungi. The volume also highlights the...

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Bibliographic Details
Other Authors: Nayak, Suraja Kumar (Editor), Baliyarsingh, Bighneswar (Editor), Singh, Ashutosh (Editor), Mannazzu, Ilaria (Editor)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2022, 2022
Edition:1st ed. 2022
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Chapter 1. Plant Growth Promoting Rhizobacteria for Sustainable Agriculture
  • Chapter 2. Plant Microbes Interactions and Its Effect on Crop Productivity
  • Chapter 3. Rhizobacterial biostimulants: efficacy in enhanced productivity and sustainable agriculture
  • Chapter 4. The Role of Arbuscular Mycorrhiza in Sustainable Agriculture
  • Chapter 5. Biocontrol Efficacy of Biomass and Secondary Metabolites of P. fluorescens Against Predominant Pest Affecting Agricultural Fields
  • Chapter 6. Exopolysaccharide-producing Azotobacter for bioremediation of heavy metal-contaminated soil
  • Chapter 7. Utilization of Arbuscular Mycorrhizal Fungi to boom the Efficiency and Product nature of Horticultural Crops
  • Chapter 8. Microbial Remediation of Persistent Agrochemicals
  • Chapter 9. Microbes Based Pesticides for Insect Pest Control and Their Management
  • Chapter 10. In-silico Tools and Approach of CRISPR Application in Agriculture
  • Chapter 11. Application of Bioinformatics in the Plant Pathology Research
  • Chapter 12. New Age Genomic Measures for Uncovering Plant-Microbiome Interactions: Tools, Pipelines and Guidance Map for Genomic Data Mining
  • Chapter 13. Bioinformatics: A Tool for Sustainable Agriculture
  • Chapter 14. Recent Advances in Deep Learning CNN Models for Plant Disease Detection