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 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.
Abstract (translated)
这项研究介绍了一种创新的人工智能驱动的精度农业系统，利用YOLOv8进行疾病识别和Retrieval Augmented Generation（RAG）进行上下文感知诊断，重点解决影响印度卡纳塔克咖啡生产领域的疾病挑战。系统将先进的物体检测技术与语言模型相结合，以解决LLMs固有的限制。我们的方法不仅解决了LLMs中的幻觉问题，还引入了动态疾病识别和修复策略。实时监测、合作数据扩展和组织的参与确保了系统的适应性在不同的农业环境中。建议的系统的效果不仅超越了自动化，还旨在确保粮食供应、保护生计和促进可持续的环保农业实践。通过促进精确疾病识别，系统为可持续和环保的农业做出了贡献，减少了农药的依赖。展望未来，该项目愿景在RAG集成的物体检测系统中持续发展，强调可扩展性、可靠性和易用性。这项研究旨在成为农业领域积极变革的灯塔，与全球致力于可持续和科技增强食品生产的努力相一致。
URL
https://arxiv.org/abs/2405.01310
