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.