Agricultural monitoring and control in edge computing has emerged as a significant research area, focusing on leveraging edge-based intelligence to enable real-time monitoring, decision-making, and automation in smart farming environments. Research papers in this domain explore the deployment of IoT sensors, drones, and edge devices for soil monitoring, crop health assessment, irrigation control, pest detection, and climate condition analysis. Studies emphasize low-latency processing, energy-efficient data aggregation, and intelligent decision-making at the edge to reduce dependence on cloud infrastructure and ensure timely interventions. Recent works integrate machine learning, deep learning, and federated learning techniques at edge nodes for predictive analytics, anomaly detection, and automated control actions, enabling precision agriculture and sustainable resource management. Security- and privacy-aware frameworks are also being developed to protect sensitive farm and environmental data. Additionally, hierarchical edge–fog–cloud architectures and workload-aware resource management strategies are explored to enhance scalability, reliability, and resilience in large-scale agricultural deployments. Overall, edge computing for agricultural monitoring and control provides intelligent, real-time, and resource-efficient solutions that improve productivity, sustainability, and operational efficiency in modern farming practices.