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 remediation strategies. Real-time monitoring, collaborative dataset expansion, and organizational involvement ensure the system's adaptability in diverse agricultural settings. The effect of the suggested system extends beyond automation, aiming to secure food supplies, protect livelihoods, and promote eco-friendly farming practices. By facilitating precise disease identification, the system contributes to sustainable and environmentally conscious agriculture, reducing reliance on pesticides. Looking to the future, the project envisions continuous development in RAG-integrated object detection systems, emphasizing scalability, reliability, and usability. This research strives to be a beacon for positive change in agriculture, aligning with global efforts toward sustainable and technologically enhanced food production.

翻译：本研究提出了一种创新的人工智能驱动精准农业系统，利用YOLOv8进行病害识别，并结合检索增强生成（RAG）实现上下文感知诊断。针对影响卡纳塔克邦咖啡产业的多类病害难题，该系统将先进的目标检测技术与语言模型相融合，以解决大型语言模型（LLM）固有的局限性。我们的方法不仅消除了LLM的幻觉问题，还引入了动态病害识别与治理策略。通过实时监测、协作式数据集扩展及组织参与，确保了系统在多样化农业场景中的适应性。该系统的效用超越了自动化范畴，旨在保障粮食供给、保护农民生计并推广生态友好型农耕实践。通过实现精准病害识别，系统助力于可持续且环境友好的农业模式，降低了农药依赖。展望未来，项目规划持续推进RAG集成目标检测系统的发展，重点关注可扩展性、可靠性与易用性。本研究致力于成为农业积极变革的灯塔，与全球范围内推动可持续及技术增强型粮食生产的努力同频共振。