PRE-MEETING WORKSHOP: Tackling Global Challenges in Plant Pathology with AI: Forecasting, Imaging, and Protein Prediction

Ticketed event; preregistration required

Description: Artificial intelligence (AI) and machine learning (ML) are cutting-edge tools that provide solutions to global challenges in plant pathology. These technologies have enhanced prediction accuracy in epidemic forecasts, allowing researchers to anticipate disease outbreaks with greater precision. They also identify complex patterns in multidimensional datasets, integrating diverse features to uncover trends that were hardly captured in less complex models. Moreover, AI and ML have played a pivotal role in developing systems that identify plant diseases from images, empowering non-experts to obtain accurate disease predictions instantly through mobile devices. Breakthroughs in bioinformatics have further enabled protein structure predictions with near-experimental accuracy, facilitating advancements in molecular plant-pathogen interaction studies and the discovery of new molecules. Together, these innovations have strengthened epidemiological forecasting, democratized disease classification, and accelerated research at the molecular level.

Plant Health 2025 Theme: This workshop aligns with the theme "Global Communities Collaborating to Address Global Risks" by highlighting the potential of AI and ML in advancing plant health. These technologies foster global collaboration by enabling researchers to address shared challenges such as epidemic forecasting, image-based disease diagnostics, and protein structure predictions. Participants will gain hands-on experience with cutting-edge tools, facilitating practical implementation while leveraging advances in statistics and computer sciences, fields renowned for their collaborative nature. This submission underscores the power of innovative technologies and multidisciplinary action in tackling global plant health risks effectively.

Target Audience: This workshop will be especially valuable for plant pathologists interested in AI to advance their research and practical applications in plant health.