Imagine a farm where drones autonomously scan fields, assessing crop health in real-time. This isn't science fiction—it's the power of Digital Twins (DTs). These virtual replicas of physical farms are revolutionizing agriculture, but their complex decision-making processes can be hard to grasp. Now, Large Language Models (LLMs), the brains behind AI chatbots, are stepping in to explain these intricate operations. This innovative approach uses a technique called Retrieval Augmented Generation (RAG), which allows LLMs to access a knowledge base of farm-specific information. When a drone makes a decision, like choosing which area to inspect next, the LLM can explain the reasoning behind it in clear, understandable language. This transparency is crucial for farmers who need to trust the system's judgment. For example, if a drone decides to inspect a specific area, the LLM can explain that the area's low confidence score triggered the inspection, indicating a potential crop health issue. This technology isn't just about explaining drone flight paths; it's about empowering farmers with real-time insights into their crops, optimizing resource allocation, and ultimately, increasing yields. While challenges remain, such as ensuring the accuracy of LLM-generated explanations, this research opens exciting possibilities for the future of smart farming. As AI and DTs continue to evolve, we can expect even more sophisticated explanations that will further bridge the gap between complex data and human understanding, leading to more sustainable and efficient agricultural practices.