Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation
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 learnin...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Annual Reviews
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/119779 |
| _version_ | 1855521494384246784 |
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| author | Garrett, K.A. Bebber, D.P. Etherton, B.A. Gold, K.M. Plex Sulá, A.I. Selvaraj, Michael G. |
| author_browse | Bebber, D.P. Etherton, B.A. Garrett, K.A. Gold, K.M. Plex Sulá, A.I. Selvaraj, Michael G. |
| author_facet | Garrett, K.A. Bebber, D.P. Etherton, B.A. Gold, K.M. Plex Sulá, A.I. Selvaraj, Michael G. |
| author_sort | Garrett, K.A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace119779 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Annual Reviews |
| publisherStr | Annual Reviews |
| record_format | dspace |
| spelling | CGSpace1197792024-08-27T10:35:31Z Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation Garrett, K.A. Bebber, D.P. Etherton, B.A. Gold, K.M. Plex Sulá, A.I. Selvaraj, Michael G. climate change mitigation pathogens artificial intelligence access to information mitigación del cambio climático organismos patógenos inteligencia artificial 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. 2022-08-26 2022-06-08T13:25:50Z 2022-06-08T13:25:50Z Journal Article https://hdl.handle.net/10568/119779 en Limited Access Annual Reviews Garrett, K.A.; Bebber, D.P.; Etherton, B.A.; Gold, K.M.; Plex Sulá, A.I.; Selvaraj, M.G. (2022) Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation. Annual Review of Phytopathology, Online first paper (01 June 2022). ISSN: 0066-4286 |
| spellingShingle | climate change mitigation pathogens artificial intelligence access to information mitigación del cambio climático organismos patógenos inteligencia artificial Garrett, K.A. Bebber, D.P. Etherton, B.A. Gold, K.M. Plex Sulá, A.I. Selvaraj, Michael G. Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation |
| title | Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation |
| title_full | Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation |
| title_fullStr | Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation |
| title_full_unstemmed | Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation |
| title_short | Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation |
| title_sort | climate change effects on pathogen emergence artificial intelligence to translate big data for mitigation |
| topic | climate change mitigation pathogens artificial intelligence access to information mitigación del cambio climático organismos patógenos inteligencia artificial |
| url | https://hdl.handle.net/10568/119779 |
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