Home > Books > Book

AI for Sustainable Biological Systems

Kyla Latrice Tennin (University of Phoenix, USA & United Nations, USA & International Research Institute for Economics and Management, USA), Rohit Kumar Pal (Rice Education University & National Geographic, India), and Mitali Talukdar (Amity University, India)
Projected Release Date: July, 2026 | Copyright: © 2027 | Pages: 525

Publication Status: Coming Soon
ISBN13: 9798260026915
ISBN13 Softcover: 9798260026922
EISBN13: 9798260026939
DOI: 10.4018/979-8-2600-2691-5

Description:

Artificial intelligence (AI) transforms the study and management of biological systems by enabling more efficient analysis, prediction, and decision-making across ecological and biological processes. As global challenges intensify, AI promotes sustainability in agriculture, conservation, ecosystem management, and biotechnology. By integrating large-scale biological data with advanced machine learning and computational models, AI optimizes resource use, improves environmental monitoring, and supports evidence-based solutions for sustainable development. The application of AI to sustainable biological systems represents an interdisciplinary approach that combines technological innovation with ecological responsibility to address pressing challenges facing society.

AI for Sustainable Biological Systems explores how AI helps manage and optimize biological systems in ways that support environmental sustainability and resource efficiency. It examines the use of AI in areas like agriculture, biodiversity conservation, ecosystem monitoring, and biotechnology, as well as its potential to address global challenges related to climate change, food security, and ecological resilience. This book covers topics such as environmental science, sustainable development, and wildlife management, and is a useful resource for biologists, engineers, academicians, researchers, and scientists.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Agriculture Science
  • Artificial Intelligence (AI)
  • Biodiversity Monitoring
  • Biological Systems
  • Biology
  • Biopharmaceuticals
  • Bird Species Classification
  • Crop Yield
  • Deep Learning
  • DNA & RNA Sequence Analysis
  • Environmental Science
  • Farming Systems
  • Hybrid Machine Learning Models
  • Plant Disease Detection
  • Socio-Ecological Responsibility
  • Sustainable Development
  • Wildlife Management

Search this Book:
Reset

All IGI Global Scientific Publishing content is archived via the CLOCKSS and LOCKSS initiative. Additionally, all IGI Global Scientific Publishing published content is available in the IGI Global Scientific Publishing InfoSci® platform.

We are committed to continually improving our platform to meet WCAG standards. We have used automated scans as well as manual review to identify and resolve compatibility issues. Our goal is to ensure all of our content is easily accessible to all users.

  • Current Accessibility Implementations
  • Screen reader compatible web pages with properly labeled elements.
  • Text alternatives for non-text content so it can be changed into large print, braille, speech, symbols, or simpler language.
  • User interface can be navigated using only a keyboard - no keyboard traps.
  • Consistent navigation on all web pages.
  • Meaningful section heading are used to organize content in a logical manner.
  • Logical focus order of elements on each web page.
  • No web pages contain any flashing, or design elements that are known to cause seizures or physical reactions.
  • Text has high contrast, with a contrast ratio of at least 4.5:1.
  • Responsive design, with text that can be resized without loss of content or functionality.
Learn More