The integration of edge computing and artificial intelligence (AI) within the Agricultural Internet of Things (IoT) is revolutionizing modern farming by enabling smart, realtime, and autonomous agricultural operations. Edge computing enables real-time data processing at the source within farms and remote agricultural areas significantly reducing latency and dependency on centralized cloud infrastructure. When combined with lightweight AI models deployed on edge devices, this approach allows for rapid, localized decision-making in critical tasks such as irrigation scheduling, pest detection, crop monitoring, and yield forecasting. The chapter discusses the architecture of agricultural edge systems, including sensor networks, edge gateways, and cloud platforms, while emphasizing data security, energy efficiency, and interoperability. Real-world case studies demonstrate the practical benefits of edge-AI systems in applications like livestock monitoring, soil health analysis, and weather prediction. Furthermore, the chapter explores strategies for optimizing resources, such as predictive maintenance and intelligent energy management, to promote sustainability. By enabling responsive, autonomous agricultural operations, the convergence of edge computing and AI supports precision farming, enhances productivity, and reduces environmental impact. This fusion marks a significant advancement toward resilient and sustainable agriculture capable of addressing the challenges of global food security and climate change.