Diseases in crops cause significant economic losses by decreasing both the yield and quality of produce. Early and accurate diagnosis of these diseases is crucial, as it enhances the effectiveness of management strategies and helps prevent long-term damage to infected plants. The need for precise, swift, and efficient methods for diagnosing diseases and detecting pathogens remains a top priority. Conventional to Emerging Advances in Plant Disease Diagnosis and Pathogen Detection comprehensively addresses key diagnostic methods and pathogen detection techniques, from their origins to the present, guiding us toward the future of plant diagnostics. It serves as a valuable resource for academics, extension workers, and growers, offering the latest information on developments in disease diagnosis and pathogen detection.
The chapters within this volume offer a comprehensive journey through the historical perspectives and groundbreaking innovations that have shaped disease diagnosis. We traverse the conventional methods that have laid the foundation for our understanding, and then embark on a journey through emerging technologies that promise to revolutionize the field. This compilation aims to connect traditional knowledge with the latest technological advancements, offering a comprehensive view of the trajectory of plant disease diagnosis. Created through collaborative efforts of field experts sharing insights and experiences, this resource is not only informative but also forward-thinking.
Features
Comprehensive coverage of both traditional and contemporary diagnostic techniques for identifying plant diseases and detecting pathogens. Detailed exploration of the latest advancements and innovations in disease diagnosis and pathogen detection, providing up-to-date knowledge. In-depth discussion of cutting-edge diagnostic strategies, including artificial intelligence, machine learning, biosensors, next-generation sequencing, point-of-care assays, and CRISPR-Cas systems. Abundantly illustrated with diagrams, flowcharts, and representations to support field identification and precise diagnosis of plant diseases.
