AI-Driven Predictive Analytics for Crop Health Management

By bridging state-of-the-art machine learning with practical, field-proven farming applications, this comprehensive volume gives researchers, professionals, and policymakers the roadmap they need to deploy AI solutions that boost crop yields, cut waste, and secure a sustainable agricultural future.

Agriculture today faces complex challenges driven by climate variability, pest outbreaks, soil degradation, and water scarcity. Feeding a growing population while protecting our environment is a complex challenge. AI applications in agriculture have developed significantly in recent years, moving from experimental models to field-level deployment. Techniques such as convolutional neural networks, decision tree-based models and ensemble forecasting methods are being used to manage crop health, predict outcomes and optimize inputs. These technologies are reshaping farm decision-making, promoting efficiency, reducing losses and supporting environmental sustainability. This book captures this evolution, contextualizing the role of AI within the broader agri-tech industry. Divided into three comprehensive sections, it introduces the fundamental principles of AI and machine learning, highlights practical applications that demonstrate how these technologies are implemented on the ground, and provides real-world case studies from diverse farming systems, offering insights into challenges, outcomes and lessons learned from field-level adoption of AI tools. Bringing together experts from agriculture, computer science, and environmental science makes this volume an invaluable reference for researchers, professionals, and policymakers aiming to integrate AI into sustainable crop health management practices.

Readers will find the volume:

• Focuses on AI-driven approaches to crop disease detection, yield prediction, and pest control;
• Combines theoretical knowledge with applied case studies across real agricultural systems;
• Integrates AI with IoT, robotics, and environmental sensing technologies;
• Suitable for readers from both technical and non-technical agricultural backgrounds.

Audience

AI researchers, environmental engineers, agribusiness professionals, policymakers, and postgraduate students. It bridges academia and industry by presenting rigorous content for both researchers and practitioners looking to innovate in the fields of agricultural and data science.

Sachi Nandan Mohanty, PhD is an Associate Professor in the School of Computer Science and Engineering at the Vellore Institute of Technology, Andhra Pradesh, India. He has published 42 books and more than 120 articles in international journals of repute. His research interests include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence.

Prity Kumari, PhD is an Assistant Professor in the College of Horticulture at Anand Agricultural University, Gujarat, India. She has published several research papers in reputable international journals and conferences, as well as several books and book chapters that reflect her academic credit. Her research spans time series analysis, cutting-edge deep learning AI techniques, image analysis, and applications of AI in agriculture.

Sarita Mohanty, PhD is an Assistant Professor in the Department of Master in Computer Application in the Centre for Post Graduate Study at the Odisha University of Agriculture and Technology, India, with more than ten years of experience. She has authored and edited several books. Her research interests include digital forensics and cybersecurity.

Shu Hu, PhD is an Assistant Professor in the Department of Computer and Information Technology and the Director of the Purdue Machine Learning and Media Forensics Lab at Purdue University, USA. He has more than 100 publications to his credit, including numerous articles in international journals and conferences of repute. His research focuses on machine learning, media forensics, and computer vision.