Chemometrics-Driven Advanced Characterization and Machine Learning for Plant Pathogen Detection and Management in Complex Ecosystems

The intersection between analytical chemistry and computational modeling with plant pathology is gaining increasing attention, especially in the drive to enhance precision agriculture and ecosystem resilience. The integration of chemometrics-driven advanced characterization and machine learning presents promising avenues for improving pathogen detection and management, particularly in understudied environments and species. Despite significant advances, challenges remain in pathogen identification, tracking, and management that warrant deeper investigation, particularly when tackling diverse plant pathogens across complex ecosystems.Recent studies have demonstrated the potential of spectroscopic and chromatographic techniques enhanced by machine learning algorithms for robust pathogen identification. These developments have resulted in improved effectiveness in understanding pathogen behavior, though they still face limitations in scope and applicability, especially concerning ecosystem variability and the specificity of pathogen-host interactions.This Research Topic aims to bridge the innovative approaches in analytical chemistry, computational modeling, and plant pathology—targeting enhanced pathogen detection and management in complex ecosystems. Key objectives include addressing specific questions about pathogen interactions, testing hypotheses on machine learning-augmented spectroscopic methods, and understanding the ecological and evolutionary aspects of plant-pathogen relationships through interdisciplinary methods.To gather further insights, we welcome articles addressing, but not limited to, the following themes:- The use of chemometrics to enhance pathogen identification in diverse plant-pathogen interactions.- Machine learning applications in predicting pathogen behavior and ecosystem impact.- Integration of spectroscopic and chromatographic techniques in pathogen tracking and management.- Genetic and epigenetic dynamics affecting pathogen resistance and resilience.- The role of technological innovations in enhancing precision agriculture and ecosystem sustainability.For article submissions, quantitative analysis needs a minimum of three biological replicates to ensure statistical significance. Articles should provide valuable insights into molecular mechanisms and address the relevant United Nations Sustainable Development Goals. Topic editors: Mourad Kharbach, HUIWEN YU Source: https://www.frontiersin.org/research-topics/72207/chemometrics-driven-advanced-characterization-and-machine-learning-for-plant-pathogen-detection-and-management-in-complex-ecosystems