Precision agriculture (PA), leveraging wireless sensor networks (WSN) for efficient data collection, is set to revolutionize intelligent farming. However, challenges such as energy efficiency, data quality, redundant data transmission, latency, and limited WSN lifespan persist. We propose an edge computing-enabled WSN framework for PA designed to enhance network longevity by optimizing energy use through controlled data redundancy and minimized data transmission to the sink. This framework involves a two-step data aggregation process: within clusters, where the cluster head (CH) aggregates data, and at a central network point, where an edge computing-enabled gateway node (GN) performs further aggregation. Our MATLAB simulation evaluates the proposed approach against the Low-energy adaptive clustering hierarchy (LEACH) protocol and two classic sensing strategies, Periodically Sensing with All Nodes (PSAN) and Effective Node Sensing (ESN). Results reveal significant energy efficiency and network lifespan improvements. Due to reduced long-range transmissions, nodes in our scheme dissipate energy over 1500 rounds, compared to 500 rounds in LEACH. Our method sends Data packets to the CH and GN for 3000 rounds, while LEACH stops at 1500 rounds. Our approach improves network stability and lifetime, with the first node dying at 790 rounds versus 500 rounds in LEACH and the last node remaining functional until 3000 rounds compared to 1500 rounds in LEACH. Our edge computing-driven aggregation technique (ECDAT) outperforms PSAN and ESN in latency, energy usage, and QoD. At 50 Mbps, ECDAT improves communication latency by 10% over ESN and 20% over PSAN. ECDAT maintains a QoD of 100% across various valid sensor and node counts, surpassing ESN and PSAN. Our contribution integrates edge computing with WSN for PA, enhancing energy utilization and data aggregation. This approach effectively tackles data redundancy, transmission efficiency, and network longevity, providing a robust solution for precision agriculture.