Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation

arXiv:2405.01310v1 Announce Type: cross
Abstract: This research introduces an innovative AI-driven precision agriculture system, leveraging YOLOv8 for disease identification and Retrieval Augmented Generation (RAG) for context-aware diagnosis. Focused on addressing the challenges of diseases affecting the coffee production sector in Karnataka, The system integrates sophisticated object detection techniques with language models to address the inherent constraints associated with Large Language Models (LLMs). Our methodology not only tackles the issue of hallucinations in LLMs, but also introduces dynamic disease identification and …

abstract agriculture arxiv challenges coffee context cs.cl cs.ir detection diagnosis disease diseases identification karnataka llm object precision production rag research retrieval retrieval augmented generation sector type yolov8