| المؤلفون: |
Garrett KA; Plant Pathology Department, University of Florida, Gainesville, Florida, USA; email: karengarrett@ufl.edu.; Food Systems Institute, University of Florida, Gainesville, Florida, USA.; Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA., Bebber DP; Department of Biosciences, University of Exeter, Exeter, United Kingdom., Etherton BA; Plant Pathology Department, University of Florida, Gainesville, Florida, USA; email: karengarrett@ufl.edu.; Food Systems Institute, University of Florida, Gainesville, Florida, USA.; Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA., Gold KM; Plant Pathology and Plant Microbe Biology Section, School of Integrative Plant Sciences, Cornell AgriTech, Cornell University, Geneva, New York, USA., Plex Sulá AI; Plant Pathology Department, University of Florida, Gainesville, Florida, USA; email: karengarrett@ufl.edu.; Food Systems Institute, University of Florida, Gainesville, Florida, USA.; Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA., Selvaraj MG; The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), Cali, Colombia. |
| مستخلص: |
Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change. |
