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This research introduces the innovative AI-driven dashboard, leveraging Transfer Learning on Efficient Net and Retrieval Augmented Generation (RAG) for recommendation generation. The research is focused on minimizing the losses incurred due to diseases thus increasing the crop yield to meet the increasing demand. Our system incorporates the pre-trained Efficient Net model to classify the leaf images into various disease categories while LLAMA 3.1 8B LLM model is used to generate the remedy insights. Our methodology not only tackles the issue of hallucinations as well as correctness of the information which are very common in LLM response generation, but provides a personalized remedy plan for the farmer incorporating climatic conditions such as Temperature and Humidity as well. The suggested methodology not only aims at detecting diseases at early stages, but also at securing the necessary food supply, reducing the amount of pesticides used and promoting eco-friendly way of cultivation. Looking at the future, the project visions of increasing the use of LLMs in the agriculture industry by continuously upgrading RAG as well as the supplied documents to maintain up to date responses.

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Retrieval-Augmented Generation for Grape Leaf Disease Diagnosis and Treatment: A Deep Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Sonali Patil,
  • Adwait Jadhav,
  • Sahil Bhavsar,
  • Pradnya Kamble,
  • Arya Tandale

摘要

This research introduces the innovative AI-driven dashboard, leveraging Transfer Learning on Efficient Net and Retrieval Augmented Generation (RAG) for recommendation generation. The research is focused on minimizing the losses incurred due to diseases thus increasing the crop yield to meet the increasing demand. Our system incorporates the pre-trained Efficient Net model to classify the leaf images into various disease categories while LLAMA 3.1 8B LLM model is used to generate the remedy insights. Our methodology not only tackles the issue of hallucinations as well as correctness of the information which are very common in LLM response generation, but provides a personalized remedy plan for the farmer incorporating climatic conditions such as Temperature and Humidity as well. The suggested methodology not only aims at detecting diseases at early stages, but also at securing the necessary food supply, reducing the amount of pesticides used and promoting eco-friendly way of cultivation. Looking at the future, the project visions of increasing the use of LLMs in the agriculture industry by continuously upgrading RAG as well as the supplied documents to maintain up to date responses.