Dense IoT implementations incur heavy data load on the implemented networks. In this paper, we propose and evaluate a low-latency method of increasing the packet throughput in agricultural IoT implementations. The proposed method envisions removal of node identifiers from packets before transmission and predictive packet-source mapping method within the edge layer of an agrarian Internet of Things (IoT) implementation. The edge layer following a master-slave architecture. Pre-trained lightweight machine learning models at the edge identify the origin of the incoming packets based on the long-term learned collective variations of the sensorial values from the slave node. This reduction in packets significantly frees up time-slots at the receiving master node, allowing for more simultaneous connections to it. This intra-edge packet origin mapping scheme is further compared with the approach of edge node identification at a remote server to adjudge the tradeoffs between accuracy and latency of transmission. The proposed method doubles the amount of sensor data transmitted between the slave to master nodes with significant energy savings over longer duration and increases the data throughput by approximately 1.5 times between the master node and the remote server for our implementation. The proposed method estimates energy savings in the order of 20 watts for a deployment setup of 100 nodes over a year. The energy savings over densely deployed IoT networks can be utilized to accommodate more nodes and increase the lifetime of the network.