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In Burkina Faso, farmers and plant pathologists remain with important challenges in treating cotton diseases. The development of natural language algorithms has advantages to the implementation of a tool for suggesting treatments for cotton diseases and the classification of cotton diseases using meteorological data. In order to propose a tool for suggesting appropriate treatments for diseases using large language models (LLM), this chapter uses meteorological data collected by the National Agency of Meteorology of Burkina Faso (ANAM-BF) to 2014 and 2023 in addition to knowledge bases on cotton diseases. In this study, we compare the Llama2 model with the RAG system to the BERT model for classification and proposed of cotton disease treatments. The result of our approach obtains a 95.4% classification precision for cotton diseases with weather data. Users may interact with the tools to generate treatments for cotton diseases with the use of a console for Llama2 with RAG and a chatbot for BERT. The performance of Llama2 model with RAG to generate appropriate responses to cotton diseases in Burkina Faso was evaluated by comparing it with GPT.

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A RAG-Based LLM for Real-Time Cotton Diseases Prediction and Suitable Treatment Suggestion

  • 会议论文

  • 发表日期：

    2026年

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  • Zakaria Kinda,
  • Sadouanouan Malo

摘要

In Burkina Faso, farmers and plant pathologists remain with important challenges in treating cotton diseases. The development of natural language algorithms has advantages to the implementation of a tool for suggesting treatments for cotton diseases and the classification of cotton diseases using meteorological data. In order to propose a tool for suggesting appropriate treatments for diseases using large language models (LLM), this chapter uses meteorological data collected by the National Agency of Meteorology of Burkina Faso (ANAM-BF) to 2014 and 2023 in addition to knowledge bases on cotton diseases. In this study, we compare the Llama2 model with the RAG system to the BERT model for classification and proposed of cotton disease treatments. The result of our approach obtains a 95.4% classification precision for cotton diseases with weather data. Users may interact with the tools to generate treatments for cotton diseases with the use of a console for Llama2 with RAG and a chatbot for BERT. The performance of Llama2 model with RAG to generate appropriate responses to cotton diseases in Burkina Faso was evaluated by comparing it with GPT.