Research on Agricultural Monitoring and Control in Edge Computing focuses on deploying distributed edge devices and intelligent computing frameworks to monitor crops, soil, weather, and irrigation systems in real-time, enabling precision agriculture and optimized resource utilization. This area addresses challenges such as heterogeneous sensors, intermittent connectivity, energy constraints, and the need for low-latency decision-making to support timely interventions. Key research directions include edge-based data aggregation and analytics for soil moisture, nutrient levels, and crop health, AI- and machine learning-driven predictive modeling for yield estimation and pest detection, and real-time control of irrigation and climate systems. Other emerging topics involve IoT–edge–cloud collaborative architectures for scalable farm management, energy-efficient sensor and computation deployment, and adaptive resource allocation for dynamic agricultural environments. Additionally, research on privacy-preserving agricultural data collection, fault-tolerant edge systems, and automated decision-making frameworks represents significant avenues for advancing intelligent, efficient, and sustainable agricultural monitoring and control solutions.