Imagine a world where AI can diagnose crop diseases, optimize planting strategies, and even predict harvests with incredible accuracy. That future is closer than you think. Large language and vision models (LLMs and LVMs) are transforming agriculture, offering solutions to some of the industry's most pressing challenges. These AI powerhouses can analyze images to detect pests and diseases, assess soil and seed quality, and even automate tasks like weeding and harvesting. One of the most exciting developments is the emergence of multimodal LLMs (MLLMs), which combine visual and textual data. These sophisticated models can answer complex questions about crop health, guide treatment strategies, and provide decision-making support for farmers. But there are hurdles to overcome. Gathering and labeling agricultural data is complex and expensive, and AI models can struggle with the real-world variability of farms and crops. Ethical concerns around data privacy and responsible AI usage also need careful attention. Still, the potential benefits are enormous. From improving crop yields and reducing pesticide use to enhancing food security and empowering farmers with data-driven insights, AI is poised to play a central role in the future of agriculture. Researchers are actively working on improving data efficiency, optimizing model performance, and addressing ethical challenges to make AI a truly transformative force in farming.