The intersection of computational modeling between analytical chemistry and plant pathology has attracted attention, particularly in its drive to enhance precision agriculture and ecosystem resilience. The integration of chemical measurement-driven advanced characterization and machine learning provides a promising tool for improving pathogen detection and management, especially in unstudied environments and species. Despite great advances, challenges remain in identifying, tracking, and managing pathogens that ensure deeper investigation, especially 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 improved effectiveness in understanding pathogen behavior, but face limitations in scope and applicability, particularly with regard to ecosystem variability and specificity of pathogen host interactions.
This research topic aims to fill innovative approaches in analytical chemistry, computational modeling and plant pathology. This is to target enhanced pathogen detection and management in complex ecosystems. Key objectives include addressing specific questions about pathogen interactions, testing hypotheses about advanced spectroscopy of machine learning, and understanding ecological and evolutionary aspects of plant-pathogen relationships through interdisciplinary methods.
To gather further insights, articles addressing the following topics, but not limited to:
– Use of chemoscopy to enhance pathogen identification in diverse plant pathogen interactions.
– Machine learning applications that predict 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 innovation in improving precision agriculture and ecosystem sustainability.
For article submissions, quantitative analysis requires a minimum of three biological replicates to ensure statistical significance. The article should provide valuable insight into molecular mechanisms and address relevant UN Sustainable Development Goals.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Brief Research Report
- Data Report
- Editorial
- Hypothesis and Theory
- Methods
- Mini Review
- Opinion
- Original Research
- Perspective
keyword: Chemoscosalization, plant pathology, machine learning, spectroscopy, pathogen management
Important note: All contributions to this research topic must be within the scope of the submitted sections and journals, as defined in the mission statement. Frontier reserves the right to direct out-of-scope manuscripts into a more appropriate section or journal at any stage of peer review.
