Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases
This report presents a comprehensive overview of the real-time media analysis system developed to assess risks associated with the top five prioritized pests and diseases affecting crops. The activity, under Work Package 2 of the CGIAR Research Initiative on Plant Health, utilizes advanced text mini...
| Autores principales: | , , , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/172706 |
| _version_ | 1855521054631395328 |
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| author | Kim, Soonho Song, Xingyi Park, Boyeong Ko, Daeun Liu, Yanyan |
| author_browse | Kim, Soonho Ko, Daeun Liu, Yanyan Park, Boyeong Song, Xingyi |
| author_facet | Kim, Soonho Song, Xingyi Park, Boyeong Ko, Daeun Liu, Yanyan |
| author_sort | Kim, Soonho |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This report presents a comprehensive overview of the real-time media analysis system developed to assess risks associated with the top five prioritized pests and diseases affecting crops. The activity, under Work Package 2 of the CGIAR Research Initiative on Plant Health, utilizes advanced text mining and machine learning techniques, including a Large Language Model (LLM), to process and analyze media articles. Key achievements include the development of an automated media analysis pipeline to monitor pests and diseases globally, the integration of GPT-4 to classify and extract detailed information from news articles, the creation of a public, interactive Crop Disease Dashboard providing real-time insights, the implementation of a cloud-based interface and REST API for user-friendly interaction and integration, and the ongoing refinement of the system based on human verification and feedback. This innovative approach aims to strengthen crop health monitoring and support policymakers and researchers in mitigating the risks posed by crop diseases and pests. |
| format | Informe técnico |
| id | CGSpace172706 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1727062025-11-06T05:48:28Z Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases Kim, Soonho Song, Xingyi Park, Boyeong Ko, Daeun Liu, Yanyan artificial intelligence large language models postharvest control plant diseases plant disease control This report presents a comprehensive overview of the real-time media analysis system developed to assess risks associated with the top five prioritized pests and diseases affecting crops. The activity, under Work Package 2 of the CGIAR Research Initiative on Plant Health, utilizes advanced text mining and machine learning techniques, including a Large Language Model (LLM), to process and analyze media articles. Key achievements include the development of an automated media analysis pipeline to monitor pests and diseases globally, the integration of GPT-4 to classify and extract detailed information from news articles, the creation of a public, interactive Crop Disease Dashboard providing real-time insights, the implementation of a cloud-based interface and REST API for user-friendly interaction and integration, and the ongoing refinement of the system based on human verification and feedback. This innovative approach aims to strengthen crop health monitoring and support policymakers and researchers in mitigating the risks posed by crop diseases and pests. 2024-12-31 2025-01-31T21:20:35Z 2025-01-31T21:20:35Z Report https://hdl.handle.net/10568/172706 en https://hdl.handle.net/10568/138891 Open Access application/pdf International Food Policy Research Institute Kim, Soonho; Song, Xingyi; Park, Boyeong; Ko, Daeun; and Liu, Yanyan. 2024. Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/172706 |
| spellingShingle | artificial intelligence large language models postharvest control plant diseases plant disease control Kim, Soonho Song, Xingyi Park, Boyeong Ko, Daeun Liu, Yanyan Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases |
| title | Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases |
| title_full | Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases |
| title_fullStr | Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases |
| title_full_unstemmed | Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases |
| title_short | Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases |
| title_sort | real time media analysis using large language model llm for the top 5 prioritized pests and diseases |
| topic | artificial intelligence large language models postharvest control plant diseases plant disease control |
| url | https://hdl.handle.net/10568/172706 |
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