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Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases

Author

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  • Kim, Soonho
  • Song, Xingyi
  • Park, Boyeong
  • Ko, Daeun
  • Liu, Yanyan

Abstract

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.

Suggested Citation

  • Kim, Soonho & Song, Xingyi & Park, Boyeong & Ko, Daeun & Liu, Yanyan, 2024. "Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases," CGIAR Initative Publications 172706, International Food Policy Research Institute (IFPRI).
    • Handle: RePEc:fpr:cgiarp:172706
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    File URL: https://hdl.handle.net/10568/172706
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    Keywords

    artificial intelligence; large language models; postharvest control; plant diseases; plant disease control;
    All these keywords.

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